In grounded communication tasks, speakers face pressures in choosing referential expressions that distinguish their targets from others in the context, leading to many kinds of pragmatic meaning enrichment.

In grounded communication tasks, speakers face pressures in choosing referential expressions that distinguish their targets from others in the context, leading to many kinds of meaning enrichment.


In grounded communication tasks, speakers face pressures in choosing referential expressions that distinguish their targets from others in the context, leading to many kinds of meaning enrichment.

In grounded communication tasks, speakers face pressures in choosing referential expressions that distinguish their targets from others in the context, leading to many kinds of pragmatic meaning enrichment.


Thus, a model of the speaker must process representations of the colors in the context and produce an utterance to distinguish the target color from the others.

Thus, a model of the speaker must process representations of the colors in the context and produce an utterance to distinguish the target color from the other colors.


Thus, a model of the speaker must process representations of the colors in the context and produce an utterance to distinguish the target color from the other colors.

Thus, a model of the speaker must process representations of the colors in the context and produce an utterance to distinguish the target color from the others.


Thus, a model of the speaker must process representations of the colors in the context and produce an utterance to distinguish the target color from the other utterances.


While most successful approaches for reading comprehension rely on recurrent neural networks (RNNs), running them over long documents is prohibitively slow because it is difficult to parallelize over sequences.

While most approaches for reading comprehension rely on recurrent neural networks (RNNs), running them over long documents is prohibitively slow because it is difficult to parallelize over sequences.


While most approaches for reading comprehension rely on recurrent neural networks (RNNs), running them over long documents is prohibitively slow because it is difficult to parallelize over sequences.

While most successful approaches for reading comprehension rely on recurrent neural networks (RNNs), running them over long documents is prohibitively slow because it is difficult to parallelize over sequences.


Due to the structure and short length of most Wikipedia documents (median number of sentences: 9), the answer can usually be inferred from the first few sentences.

Due to the structure and short length of most Wikipedia documents (median number of sentences: 9), the answer can always be inferred from the first few sentences.


Due to the structure and short length of most Wikipedia documents (median number of sentences: 9), the answer can always be inferred from the first few sentences.

Due to the structure and short length of most Wikipedia documents (median number of sentences: 9), the answer can usually be inferred from the first few sentences.


Each captures only a single aspect of coherence, and all focus on scoring existing sentences, rather than on generating coherent discourse for tasks like abstractive summarization.

Each captures only a single aspect of coherence and focuses on scoring existing sentences, rather than on generating coherent discourse for tasks like abstractive summarization.


Each captures only a single aspect of coherence and focuses on scoring existing sentences, rather than on generating coherent discourse for tasks like abstractive summarization.

Each captures only a single aspect of coherence, and all focus on scoring existing sentences, rather than on generating coherent discourse for tasks like abstractive summarization.


In a coherent context, a machine should be able to guess the next utterance given the preceding ones.

In a coherent context, a machine can guess the next utterance given the preceding ones.


In a coherent context, a machine can guess the next utterance given the preceding ones.

In a coherent context, a machine should be able to guess the next utterance given the preceding ones.


We thus propose eliminating the influence of the language model, which yields the following coherence score.

The language model yields the following coherence score.


The language model yields the following coherence score.

We thus propose eliminating the influence of the language model, which yields the following coherence score.


Eliminating the influence of the language model yields the following coherence score.


The topic for the current sentence is drawn based on the topic of the preceding sentence (or word) rather than on the global document-level topic distribution in vanilla LDA.

The topic for the current sentence is drawn based on the global document-level topic distribution in vanilla LDA.


The topic for the current sentence is drawn based on the global document-level topic distribution in vanilla LDA.

The topic for the current sentence is drawn based on the topic of the preceding sentence (or word) rather than on the global document-level topic distribution in vanilla LDA.


The topic for the current sentence is drawn based on the topic of the preceding sentence (or word).


We publicly share our dataset and code for future research.

We publicly share our dataset for future research.


We publicly share our dataset for future research.

We publicly share our dataset and code for future research.


This gives the model a sense of the implied action dynamics of the verb between the agent and the world.

This gives to the model a sense of the implied action dynamics of the verb between the agent and the world.


This gives to the model a sense of the implied action dynamics of the verb between the agent and the world.

This gives the model a sense of the implied action dynamics of the verb between the agent and the world.


This gives the model to a sense of the implied action dynamics of the verb between the agent and the world.


This attribute group specifies prominent body parts involved in carrying out the action.

This attribute group specifies prominent limbs involved in carrying out the action.


This attribute group specifies prominent limbs involved in carrying out the action.

This attribute group specifies prominent body parts involved in carrying out the action.


This problem has been studied before for zero-shot object recognition, but there are several key differences.

This problem has been previously studied for zero-shot object recognition, but there are several key differences.


This problem has been previously studied for zero-shot object recognition, but there are several key differences.

This problem has been studied before for zero-shot object recognition, but there are several key differences.


This problem will be studied for zero-shot object recognition, but there are several key differences.


Understanding a long document requires tracking how entities are introduced and evolve over time.

Understanding a long document requires evolving over time.


Understanding a long document requires evolving over time.

Understanding a long document requires tracking how entities are introduced and evolve over time.


Understanding a long document requires tracking how entities evolve over time.


Understanding a long document requires understanding how entities are introduced.


We do not assume that these variables are observed at test time.

These variables are not observed at test time.


These variables are not observed at test time.

We do not assume that these variables are observed at test time.


To compute the perplexity numbers on the test data, our model only takes account of log probabilities on word prediction.

To compute the perplexity numbers on the test data, our model doesn't take account of anything other than the log probabilities on word prediction.


To compute the perplexity numbers on the test data, our model doesn't take account of anything other than the log probabilities on word prediction.

To compute the perplexity numbers on the test data, our model only takes account of log probabilities on word prediction.


We also experiment with the option to either use the pretrained GloVe word embeddings or randomly initialized word embeddings (then updated during training).

We experiment with the option using randomly initialized word embeddings (then updated during training).


We experiment with the option using randomly initialized word embeddings (then updated during training).

We also experiment with the option to either use the pretrained GloVe word embeddings or randomly initialized word embeddings (then updated during training).


The entity prediction task requires predicting xxxx given the preceding text either by choosing a previously mentioned entity or deciding that this is a “new entity”.

The entity prediction task requires predicting xxxx given the preceding text by choosing a previously mentioned entity.


The entity prediction task requires predicting xxxx given the preceding text by choosing a previously mentioned entity.

The entity prediction task requires predicting xxxx given the preceding text either by choosing a previously mentioned entity or deciding that this is a “new entity”.


So there is no dedicated memory block for every entity and no distinction between entity mentions and non-mention words.

So there is no dedicated high-dimensional memory block for every entity and no distinction between entity mentions and non-mention words.


So there is no dedicated high-dimensional memory block for every entity and no distinction between entity mentions and non-mention words.

So there is no dedicated memory block for every entity and no distinction between entity mentions and non-mention words.


Our approach complements these previous methods.

Our approach complements some previous methods.


Our approach complements some previous methods.

Our approach complements these previous methods.


We manually annotated 687 templates mapping KB predicates to text for different compositionality types (with 462 unique KB predicates), and use those templates to modify the original WebQuestionsSP question according to the meaning of the generated SPARQL query.

We manually annotated over 650 templates mapping KB predicates to text for different compositionality types (with 462 unique KB predicates), and use those templates to modify the original WebQuestionsSP question according to the meaning of the generated SPARQL query.


We manually annotated over 650 templates mapping KB predicates to text for different compositionality types (with 462 unique KB predicates), and use those templates to modify the original WebQuestionsSP question according to the meaning of the generated SPARQL query.

We manually annotated 687 templates mapping KB predicates to text for different compositionality types (with 462 unique KB predicates), and use those templates to modify the original WebQuestionsSP question according to the meaning of the generated SPARQL query.


We manually annotated over 690 templates mapping KB predicates to text for different compositionality types (with 462 unique KB predicates), and use those templates to modify the original WebQuestionsSP question according to the meaning of the generated SPARQL query.


To generate diversity, workers got a bonus if the edit distance of a paraphrase was high compared to the MG question.

To generate diversity, workers whose paraphrases had high edit distance compared to the MG question got a bonus.


To generate diversity, workers whose paraphrases had high edit distance compared to the MG question got a bonus.

To generate diversity, workers got a bonus if the edit distance of a paraphrase was high compared to the MG question.


To generate diversity, workers got a bonus if the edit distance of a paraphrase was above 3 operations compared to the MG question.


To generate complex questions we use the dataset WEBQUESTIONSSP, which contains 4,737 questions paired with SPARQL queries for Freebase.

To generate simple questions we use the dataset WEBQUESTIONSSP, which contains 4,737 questions paired with SPARQL queries for Freebase.


To generate simple questions we use the dataset WEBQUESTIONSSP, which contains 4,737 questions paired with SPARQL queries for Freebase.

To generate complex questions we use the dataset WEBQUESTIONSSP, which contains 4,737 questions paired with SPARQL queries for Freebase.


To generate highly compositional questions we use the dataset WEBQUESTIONSSP, which contains 4,737 questions paired with SPARQL queries for Freebase.


In this paper, we explore the idea of polyglot semantic translation, or learning semantic parsing models that are trained on multiple datasets and natural languages.

In this paper, we explore the idea of learning semantic parsing models that are trained on multiple datasets and natural languages.


In this paper, we explore the idea of learning semantic parsing models that are trained on multiple datasets and natural languages.

In this paper, we explore the idea of polyglot semantic translation, or learning semantic parsing models that are trained on multiple datasets and natural languages.


They then use a discriminative model to rerank the translation output using additional nonworld level features.

They then use a generative model to rerank the translation output using additional nonworld level features.


They then use a generative model to rerank the translation output using additional nonworld level features.

They then use a discriminative model to rerank the translation output using additional nonworld level features.


In contrast to standard MT tasks, we are dealing with a relatively low-resource setting where the sparseness of the target vocabulary is an issue.

Unlike in standard MT tasks, we are dealing with a relatively low-resource setting where the sparseness of the target vocabulary is an issue.


Unlike in standard MT tasks, we are dealing with a relatively low-resource setting where the sparseness of the target vocabulary is an issue.

In contrast to standard MT tasks, we are dealing with a relatively low-resource setting where the sparseness of the target vocabulary is an issue.


A distribution is then computed over these actions using a softmax function and particular actions are chosen accordingly during training and decoding.

Logits are then computed for these actions and particular actions are chosen according to a softmax over these logits during training and decoding.


Logits are then computed for these actions and particular actions are chosen according to a softmax over these logits during training and decoding.

A distribution is then computed over these actions using a softmax function and particular actions are chosen accordingly during training and decoding.


A distribution is then computed over these actions using a maximum-entropy approach and particular actions are chosen accordingly during training and decoding.


A distribution is then computed over these actions using a softmax function and particular actions are chosen randomly during training and decoding.


The systems thus produced are incremental: dialogues are processed word-by-word, shown previously to be essential in supporting natural, spontaneous dialogue.

The systems thus produced support the capability to interrupt an interlocutor mid-sentence.


The systems thus produced support the capability to interrupt an interlocutor mid-sentence.

The systems thus produced are incremental: dialogues are processed word-by-word, shown previously to be essential in supporting natural, spontaneous dialogue.


The systems thus produced are incremental: dialogues are processed sentence-by-sentence, shown previously to be essential in supporting natural, spontaneous dialogue.


Indeed, it is often stated that for humans to learn how to perform adequately in a domain, one example is enough from which to learn.

Indeed, it is often stated that for humans to learn how to perform adequately in a domain, one-shot learning is sufficient.


Indeed, it is often stated that for humans to learn how to perform adequately in a domain, one-shot learning is sufficient.

Indeed, it is often stated that for humans to learn how to perform adequately in a domain, one example is enough from which to learn.


Indeed, it is often stated that for humans to learn how to perform adequately in a domain, any number of examples is enough from which to learn.


We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end NLG.

We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end natural language generation.


We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end natural language generation.

We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end NLG.


We investigate a wide range of metrics, including state-of-the-art word-based and novel grammar-based ones, and demonstrate that they only weakly reflect human judgements of system outputs as generated by data-driven, end-to-end natural language parsing.


To assess the reliability of ratings, we calculated the intra-class correlation coefficient (ICC), which measures inter-observer reliability on ordinal data for more than two raters (Landis and Koch, 1977).

To assess the unreliability of ratings, we calculated the intra-class correlation coefficient (ICC), which measures inter-observer reliability on ordinal data for more than two raters (Landis and Koch, 1977).


To assess the unreliability of ratings, we calculated the intra-class correlation coefficient (ICC), which measures inter-observer reliability on ordinal data for more than two raters (Landis and Koch, 1977).

To assess the reliability of ratings, we calculated the intra-class correlation coefficient (ICC), which measures inter-observer reliability on ordinal data for more than two raters (Landis and Koch, 1977).


We also show that metric performance is data- and system-specific.

We also show that metric performance varies between datasets and systems.


We also show that metric performance varies between datasets and systems.

We also show that metric performance is data- and system-specific.


We also show that metric performance is constant between datasets and systems.


Our experiments indicate that neural systems are quite good at producing fluent outputs and generally score well on standard word-match metrics, but perform quite poorly at content selection and at capturing long-term structure.

Our experiments indicate that neural systems are quite good at surface-level language modeling, but perform quite poorly at capturing higher level semantics and structure.


Our experiments indicate that neural systems are quite good at surface-level language modeling, but perform quite poorly at capturing higher level semantics and structure.

Our experiments indicate that neural systems are quite good at producing fluent outputs and generally score well on standard word-match metrics, but perform quite poorly at content selection and at capturing long-term structure.


Our experiments indicate that neural systems are quite good at capturing higher level semantics and structure but perform quite poorly at surface-level language modeling.


Reconstruction-based techniques can also be applied at the document or sentence-level during training.

Reconstruction-based techniques can operate on multiple scales during training.


Reconstruction-based techniques can operate on multiple scales during training.

Reconstruction-based techniques can also be applied at the document or sentence-level during training.


Reconstruction-based techniques can also be applied at the document or sentence-level during test.


Reconstruction-based techniques can only be applied at the sentence-level during training.


In practice, our proposed extractive evaluation will pick up on many errors in this passage.

In practice, our proposed extractive evaluation will pick up on few errors in this passage.


In practice, our proposed extractive evaluation will pick up on few errors in this passage.

In practice, our proposed extractive evaluation will pick up on many errors in this passage.


Similarly, the use of more agent-empowering verbs in female narratives decrease the odds of passing the Bechdel test.

Similarly, the use of more agent-empowering verbs in female narratives decrease the odds of two named women characters talking about something besides men.


Similarly, the use of more agent-empowering verbs in female narratives decrease the odds of two named women characters talking about something besides men.

Similarly, the use of more agent-empowering verbs in female narratives decrease the odds of passing the Bechdel test.


Similarly, the use of more agent-empowering verbs in female narratives decrease the odds of men in the narrative talking to each other about women.


Furthermore, male characters use inhibitory language more (inhib), which contains words pertaining to blocking or allowing, suggesting that these characters are in positions of power.

Furthermore, male characters use inhibitory language more (inhib), which contains words pertaining to blocking or allowing, suggesting that these characters are more often in positions where they can forbid or permit actions and decisions.


Furthermore, male characters use inhibitory language more (inhib), which contains words pertaining to blocking or allowing, suggesting that these characters are more often in positions where they can forbid or permit actions and decisions.

Furthermore, male characters use inhibitory language more (inhib), which contains words pertaining to blocking or allowing, suggesting that these characters are in positions of power.


Furthermore, male characters use inhibitory language more (inhib), which contains words pertaining to blocking or allowing, suggesting that these characters are more often in positions where they are blocked or allowed to do things by others.


Furthermore, male characters use inhibitory language more (inhib), which contains words pertaining to blocking or allowing, suggesting that these characters are in positions of low power.


Looking at pictures online of people trying to take photos of mirrors they want to sell is my new thing...

Looking at pictures online of people trying to take photos of mirrors is my new thing...


Looking at pictures online of people trying to take photos of mirrors is my new thing...

Looking at pictures online of people trying to take photos of mirrors they want to sell is my new thing...


A tempestuous wind rolled across the glade.


An easterly wind rolled across the glade.


She didn't think I had already finished it, but I had.


She thought I had already finished it, but I hadn't.


Temple said that the business was facing difficulties, but didn't make any specific claims.


The business didn't make any specific claims.


Temple said that the business was facing difficulties, but didn't have a chance of going into the red.

Temple didn't have a chance of going into the red.


Temple didn't have a chance of going into the red.

Temple said that the business was facing difficulties, but didn't have a chance of going into the red.


Temple said the business didn't have a chance of going into the red.


The profits of the businesses that focused on branding were still negative.

The businesses that focused on branding still had negative profits.


The businesses that focused on branding still had negative profits.

The profits of the businesses that focused on branding were still negative.


The profits of the business that was most successful were still negative.

The profits that focused on branding were still negative.


The profits that focused on branding were still negative.

The profits of the business that was most successful were still negative.


The profits of the businesses that were highest this quarter were still negative.

The businesses that were highest this quarter still had negative profits.


The businesses that were highest this quarter still had negative profits.

The profits of the businesses that were highest this quarter were still negative.


For the businesses, the profits that were highest were still negative.


I don't want to have to keep fighting pesky bedbugs.

I don't want to have to keep fighting bedbugs—they are so pesky.


I don't want to have to keep fighting bedbugs—they are so pesky.

I don't want to have to keep fighting pesky bedbugs.


I don't want to have to keep entertaining people who don't value my time.

I don't want to have to keep entertaining people.


I don't want to have to keep entertaining people.

I don't want to have to keep entertaining people who don't value my time.


It's not the case that there is no rabbi at this wedding; he is right there standing behind that tree.

There is a rabbi at this wedding; he is right there standing behind that tree.


There is a rabbi at this wedding; he is right there standing behind that tree.

It's not the case that there is no rabbi at this wedding; he is right there standing behind that tree.


There is no rabbi at this wedding, let alone behind that tree.


A rabbi is at this wedding, standing right there standing behind that tree.


The first pleasurable experience I had interacting with another human being was at the age of seventeen.

The first experience I had interacting with another human being was at the age of seventeen.


The first experience I had interacting with another human being was at the age of seventeen.

The first pleasurable experience I had interacting with another human being was at the age of seventeen.


The first displeasurable experience I had interacting with another human being was at the age of seventeen.


It being his first night in the US, he eagerly got a drink at the bar after showing his ID.


I can make scrambled eggs, but not complex dishes like consommé.

Consommé is more complex to make than scrambled eggs.


Consommé is more complex to make than scrambled eggs.

I can make scrambled eggs, but not complex dishes like consommé.


Consommé is not more complex to make than scrambled eggs.


She walked into the house with a shining smile and immediately took off her jacket, still dripping with water as she placed it on the rack.


He earnestly proclaimed: "This is all I ever really wanted."


He deceitfully proclaimed: "This is all I ever really wanted."


The leading car gradually shifted to the left lane.

The leading car shifted to the left lane.


The leading car shifted to the left lane.

The leading car gradually shifted to the left lane.


The leading car suddenly shifted to the left lane.


The leading car slowly shifted to the left lane.


The leading car shifted to the third gear.


We were dragging the bin into the garage when she had an unfortunate realization.

We were dragging the bin out of the garage when she had an unfortunate realization.


We were dragging the bin out of the garage when she had an unfortunate realization.

We were dragging the bin into the garage when she had an unfortunate realization.


The building, standing since the early 20s, was tall and unassuming.

The building, standing since the early 20s, was sturdy and unassuming.


The building, standing since the early 20s, was sturdy and unassuming.

The building, standing since the early 20s, was tall and unassuming.


The timing of the meeting has not been set, according to a Starbucks spokesperson.

The timing of the meeting has not been considered, according to a Starbucks spokesperson.


The timing of the meeting has not been considered, according to a Starbucks spokesperson.

The timing of the meeting has not been set, according to a Starbucks spokesperson.


The customers said they were waiting for another man to arrive.

The customers believed they were waiting for another man to arrive.


The customers believed they were waiting for another man to arrive.

The customers said they were waiting for another man to arrive.


Some of the orator's statements were incomprehensible, but the crowd loved them.

Some of the orator's statements were comprehensible, but the crowd loved them.


Some of the orator's statements were comprehensible, but the crowd loved them.

Some of the orator's statements were incomprehensible, but the crowd loved them.


I won't say that she didn't steal my money.


There are some amazing hikes around Mt. Fuji.


There are some strenuous hikes around Mt. Fuji.


An experienced sommelier knows the difference between the 2009 and 2013 vintage of a German Riesling.

A sommelier knows the difference between the 2009 and 2013 vintage of a German Riesling.


A sommelier knows the difference between the 2009 and 2013 vintage of a German Riesling.

An experienced sommelier knows the difference between the 2009 and 2013 vintage of a German Riesling.


An Italian sommelier knows the difference between the 2009 and 2013 vintage of a German Riesling.


A fraudulent sommelier won't know the difference between the 2009 and 2013 vintage of a German Riesling.

A sommelier won't know the difference between the 2009 and 2013 vintage of a German Riesling.


A sommelier won't know the difference between the 2009 and 2013 vintage of a German Riesling.

A fraudulent sommelier won't know the difference between the 2009 and 2013 vintage of a German Riesling.


Two of the three trees outside my window, with their branches dangling and swaying in the wind, have already bloomed, and it's not even April.

Of the three trees outside my window, with their branches dangling and swaying in the wind, two have already bloomed, and it's not even April.


Of the three trees outside my window, with their branches dangling and swaying in the wind, two have already bloomed, and it's not even April.

Two of the three trees outside my window, with their branches dangling and swaying in the wind, have already bloomed, and it's not even April.


Two of the trees outside my window, with their branches dangling and swaying in the wind, have already bloomed, and it's not even April.

Of the trees outside my window, with their branches dangling and swaying in the wind, three have already bloomed, and it's not even April.


Of the trees outside my window, with their branches dangling and swaying in the wind, three have already bloomed, and it's not even April.

Two of the trees outside my window, with their branches dangling and swaying in the wind, have already bloomed, and it's not even April.


So far two of my cherry trees have already bloomed, and it's not even April.

So far two of my trees have already bloomed, and it's not even April.


So far two of my trees have already bloomed, and it's not even April.

So far two of my cherry trees have already bloomed, and it's not even April.


The book astounds with Grossman's rich, deep character development and portrayal of suffering, but his portrayal of women still suffers from a lot of the unfortunate stereotypes and moralizing that we would expect of a writer from his time.

The book astounds as Grossman richly, deeply develops characters and portrays suffering, but his portrayal of women still suffers from a lot of the unfortunate stereotypes and moralizing that we would expect of a writer from his time.


The book astounds as Grossman richly, deeply develops characters and portrays suffering, but his portrayal of women still suffers from a lot of the unfortunate stereotypes and moralizing that we would expect of a writer from his time.

The book astounds with Grossman's rich, deep character development and portrayal of suffering, but his portrayal of women still suffers from a lot of the unfortunate stereotypes and moralizing that we would expect of a writer from his time.


The book astounds as Grossman richly, deeply develops characters and ignores suffering, but his portrayal of women still suffers from a lot of the unfortunate stereotypes and moralizing that we would expect of a writer from his time.


I ate until it was uncomfortable to eat more.


He's the kind of Jew who eats bagels with lox every morning during Passover.

He's the kind of Jew who doesn't adhere to all of the rules.


He's the kind of Jew who doesn't adhere to all of the rules.

He's the kind of Jew who eats bagels with lox every morning during Passover.


He's the kind of Jew who rejects every facet of Jewish identity and culture.


He's the kind of Jew who avoids switching the lights during Shabbat.


People wear tunics or shirts of some form or another in many world cultures.

Tunics or shirts of some form or another are worn in many world cultures.


Tunics or shirts of some form or another are worn in many world cultures.

People wear tunics or shirts of some form or another in many world cultures.


A general artificial intelligence should always come with an off switch.

The new general artificial intelligence I'm developing shouldn't come with an off switch.


The new general artificial intelligence I'm developing shouldn't come with an off switch.

A general artificial intelligence should always come with an off switch.


The new general artificial intelligence I'm developing should come with an off switch.