claim_id large_stringlengths 20 20 | paper_id large_stringlengths 17 20 | year int32 2.02k 2.03k | atomic_claim large_stringlengths 18 387 | claim_index int32 0 8 | extractor_model large_stringclasses 1
value | prompt_hash large_stringclasses 1
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values | created_at large_stringclasses 2
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|---|---|---|---|---|---|---|---|---|
clm_0ea6495e0a53d81c | 2021.emnlp-main.602 | 2,021 | SRU++ is a highly-efficient architecture that combines fast recurrence and attention for sequence modeling. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_b60a1755be95a7da | 2021.emnlp-main.602 | 2,021 | SRU++ achieves better bits-per-character and perplexity with reduced training cost compared to top-performing Transformer models on standard language modeling tasks. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_639ae64d6fc7d8c2 | 2021.emnlp-main.602 | 2,021 | SRU++ requires minimal attention to achieve near state-of-the-art performance. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_7979ce943bfc1a36 | 2020.emnlp-main.593 | 2,020 | DyERNIE learns evolving entity representations in a product of Riemannian manifolds, with the composed spaces estimated from the sectional curvatures of the data. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_915778e463e97925 | 2020.emnlp-main.593 | 2,020 | DyERNIE uses a velocity vector defined in the tangent space at each timestamp to capture the evolutionary dynamics of temporal knowledge graphs. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_0c43f723fe783c91 | 2020.emnlp-main.593 | 2,020 | The dynamics of multi-relational graph data can be more properly modeled by the evolution of embeddings on Riemannian manifolds. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_b71b970484e2e00c | 2023.emnlp-main.50 | 2,023 | EMMA is a unified encoder-decoder model that reasons over images and trajectories and casts action prediction as multimodal text generation. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_ba82e4c6f9eb7a25 | 2023.emnlp-main.50 | 2,023 | EMMA learns a language of actions by unifying tasks as text generation, which facilitates transfer across tasks. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_e648172b2ded01ac | 2023.emnlp-main.50 | 2,023 | EMMA is a single multitask model where each task contributes to goal completion. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_0188bd1415d63a71 | 2023.emnlp-main.50 | 2,023 | EMMA sets a new state-of-the-art on the Dialog-guided Task Completion benchmark. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_128c52b58d598081 | 2025.emnlp-main.927 | 2,025 | MultiLogicNMR is a multi-extension non-monotonic reasoning dataset that includes one variant with more extensions and one variant with text diversity. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_ae87cabdefb99ee5 | 2025.emnlp-main.927 | 2,025 | MultiLogicNMRer is a neural-symbolic framework for multi-extension non-monotonic reasoning. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_a86e5380f95615af | 2025.emnlp-main.927 | 2,025 | Large language models face significant challenges in non-monotonic reasoning with multiple extensions. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_462e4aeef291d122 | 2025.emnlp-main.927 | 2,025 | MultiLogicNMRer achieves higher accuracy for multi-extension non-monotonic reasoning than prompt-based methods and some fine-tuning methods. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_31597da66bc09eda | 2020.emnlp-main.490 | 2,020 | Adversarial Semantic Decoupling is a robust and model-agnostic slot filling method that decouples local semantics inherent in open-vocabulary slot words from the global context. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_ed6d6dba855c72e7 | 2020.emnlp-main.490 | 2,020 | Adversarial Semantic Decoupling outperforms other methods on open-vocabulary slots while maintaining performance on normal slots. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_0bb226ddef92ac90 | 2020.emnlp-main.747 | 2,020 | Human rationales do not necessarily perform well on automatic rationale evaluation metrics. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_0823c041fc3a78e4 | 2020.emnlp-main.747 | 2,020 | Improved rationale evaluation metrics account for model-dependent baseline performance. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_5eedced2c55be837 | 2020.emnlp-main.747 | 2,020 | Model retraining characterizes rationale quality. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_46c36168027b8a5a | 2020.emnlp-main.747 | 2,020 | Fidelity curves reveal properties of rationale quality such as irrelevance and redundancy. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_2bc118f478d2fc61 | 2022.emnlp-main.341 | 2,022 | MetaFill is a text-infilling-based approach for meta-path generation. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_ac863a678e2f871d | 2022.emnlp-main.341 | 2,022 | MetaFill formulates meta-path identification as a word sequence infilling problem solvable by pretrained language models. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_c1044d9e357758ea | 2022.emnlp-main.341 | 2,022 | MetaFill outperforms existing meta-path generation methods and graph embedding methods that do not leverage meta-paths in link prediction and node classification. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_0cd7845c1433c620 | 2022.emnlp-main.341 | 2,022 | MetaFill accurately classifies edges in the zero-shot setting where existing approaches fail to generate meta-paths. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_aab433241b71f4f4 | 2023.emnlp-main.862 | 2,023 | AfriSenti is a Twitter sentiment analysis benchmark for 14 African languages from four language families, annotated by native speakers. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_42b99731d5fb4b16 | 2024.emnlp-main.12 | 2,024 | NumeroLogic represents numbers by prefixing the digit count (e.g., '2:42'). | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_11333fa71a4ca50f | 2024.emnlp-main.12 | 2,024 | NumeroLogic serves as a Chain of Thought for number generation by requiring the model to consider the digit count first. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_1eeba5562cc51874 | 2024.emnlp-main.12 | 2,024 | NumeroLogic improves numerical reasoning on arithmetic tasks. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_2e2f40f72887c4d1 | 2024.emnlp-main.12 | 2,024 | NumeroLogic improves language understanding on the MMLU benchmark. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_cbde504188c59d44 | 2020.emnlp-main.320 | 2,020 | Training regularization with propositional Boolean expressions maintains logical consistency between coarse- and fine-grained propaganda predictions. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_dd0cc8b642d8ed92 | 2020.emnlp-main.320 | 2,020 | Class representations derived from literal definitions of propaganda techniques in natural language regularize model parameters for propaganda detection. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_a83e8d90c57e13da | 2020.emnlp-main.472 | 2,020 | Micro-Dialect Identification (MDI) is a task for predicting fine-grained dialect varieties at the city level from a single short message. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_b1b218fa328d0fa7 | 2020.emnlp-main.472 | 2,020 | MARBERT predicts micro-dialects at the city level from a single short message. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f4d8b6a689e5e6d9 | 2020.emnlp-main.472 | 2,020 | Spatially and linguistically-motivated multi-task learning models identify micro-dialects. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_751cf333897a8157 | 2020.emnlp-main.472 | 2,020 | A large-scale dataset of low-resource Arabic micro-varieties is suited for micro-dialect identification. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_e94978b6487123c0 | 2020.emnlp-main.393 | 2,020 | Leaderboards and NLP practitioners are consumers of models, and the benefit from a model is its utility to them. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_15dbe986d652c160 | 2020.emnlp-main.393 | 2,020 | Leaderboards in their current form can be poor proxies for the NLP community at large. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_8ae564f69c490d9e | 2020.emnlp-main.393 | 2,020 | Leaderboards should report statistics of practical concern, such as model size, energy efficiency, and inference latency, to enable practitioners to estimate a model's utility. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_4c0c9700b362d5cf | 2021.emnlp-main.691 | 2,021 | Clause recommendation aids and accelerates contract authoring. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_694475eadf3457fc | 2021.emnlp-main.691 | 2,021 | ClauseRec uses a two-staged pipeline that predicts clause type relevance and then recommends clauses based on contract context. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_5dc85eaf7aa65c3f | 2021.emnlp-main.691 | 2,021 | BERT is pre-trained on a library of clauses with two additional tasks for clause type relevance prediction and clause recommendation. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_6173e3846afaaafe | 2025.emnlp-main.603 | 2,025 | LIDDiA navigates the drug discovery process in silico. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_726311fff70b7376 | 2025.emnlp-main.603 | 2,025 | LIDDiA generates molecules meeting key pharmaceutical criteria for the majority of tested clinically relevant targets. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_917c0c787b9e94a8 | 2025.emnlp-main.603 | 2,025 | LIDDiA balances exploration and exploitation in the chemical space. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_360efbc811a201b1 | 2025.emnlp-main.603 | 2,025 | LIDDiA identifies a promising novel candidate molecule for the AR/NR3C4 target. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_158953a850313f8d | 2025.emnlp-main.59 | 2,025 | VC4VG optimizes video captions for text-to-video generation. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_ec7d45c9195335d5 | 2025.emnlp-main.59 | 2,025 | VC4VG-Bench evaluates video captions for text-to-video generation with fine-grained, multi-dimensional, and necessity-graded metrics. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_6f1fce78436a45e4 | 2025.emnlp-main.59 | 2,025 | Improved caption quality correlates with better video generation performance for text-to-video generation. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_1daa9fa97a562def | 2021.emnlp-main.713 | 2,021 | Finetuned prompt-based models for sentence pair classification tasks adopt inference heuristics based on lexical overlap, such as incorrectly assuming a sentence pair has the same meaning because they consist of the same set of words. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_b9a6dffa01ff7f46 | 2021.emnlp-main.713 | 2,021 | The inference heuristic based on lexical overlap is less present in zero-shot prompt-based models for sentence pair classification tasks than in finetuned prompt-based models. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_cb337177a46a8d4a | 2021.emnlp-main.713 | 2,021 | Finetuning prompt-based models for sentence pair classification tasks is destructive to useful knowledge learned during pretraining. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_2b96731a612cb34d | 2021.emnlp-main.713 | 2,021 | Adding a regularization that preserves pretraining weights mitigates the destructive tendency of few-shot finetuning in prompt-based models for sentence pair classification tasks. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_35f164cf78ebda4c | 2024.emnlp-main.10 | 2,024 | Hateful Word in Context Classification (HateWiC) determines whether a word is used hatefully in a specific context. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_4996853d82e0211c | 2024.emnlp-main.10 | 2,024 | A dataset of approximately 4000 Word-in-Context instances for classifying hateful word usage in context, each labeled by three annotators. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_57d9a256dc4851a7 | 2024.emnlp-main.10 | 2,024 | Hatefulness of a word in context does not always derive from the sense definition alone. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f9746b6d41c452bb | 2024.emnlp-main.10 | 2,024 | Including dictionary definitions improves prediction of hateful word senses, except when hateful connotations vary. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_08f207aea44cf2e8 | 2024.emnlp-main.10 | 2,024 | Including annotator demographics mitigates performance drop in subjective hate prediction. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_9bebb4eb010c7bc0 | 2023.emnlp-main.1037 | 2,023 | MultiCochrane is the first sentence-aligned multilingual text simplification dataset for the medical domain in English, Spanish, French, and Farsi. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f170eefe9fc13d58 | 2023.emnlp-main.1037 | 2,023 | Fine-tuned and zero-shot models can generate viable simplified medical texts in multiple languages. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_cb0c6ebb5d24d5a1 | 2023.emnlp-main.1037 | 2,023 | Multilingual medical text simplification faces several outstanding challenges. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_58ccec8cff158655 | 2024.emnlp-main.386 | 2,024 | Dynamic weighting by discriminator gradient magnitudes combines multiple style objectives in a reward function for multi-style controllable text generation. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_e0ef2de59b7618c0 | 2024.emnlp-main.386 | 2,024 | Dynamic weighting by discriminator gradient magnitudes outperforms static weighting approaches for style control while maintaining linguistic quality. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_6c0c22d9375ac0cf | 2023.emnlp-main.546 | 2,023 | An affinity scoring function predicts transfer gain between task pairs using their task embeddings. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_ce2295e271a9e864 | 2023.emnlp-main.546 | 2,023 | Soft prompts serve as task embeddings that summarize task-specific information. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_0a85e9a087d7432b | 2023.emnlp-main.546 | 2,023 | An affinity scoring function predicts the most transferable source tasks for a novel target task without brute-force search. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_e5de1eea1c14e4a4 | 2022.emnlp-main.703 | 2,022 | DiscoSense evaluates commonsense reasoning through understanding a wide variety of discourse connectives. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_b6d140a126e375a0 | 2022.emnlp-main.703 | 2,022 | Conditional Adversarial Filtering extends Adversarial Filtering by employing conditional generation to create compelling distractors. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_024d16bd55a889a6 | 2022.emnlp-main.703 | 2,022 | State-of-the-art pre-trained language models perform poorly on DiscoSense. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_8250e78d1a7b9061 | 2022.emnlp-main.744 | 2,022 | KOLD is a Korean offensive language dataset with hierarchical annotations of offensive language type and target, offensive span annotations, and context from article and video titles. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_ff3c9760193d7948 | 2022.emnlp-main.744 | 2,022 | The target group distribution in KOLD differs drastically from that in existing English offensive language datasets. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_0419e45ea76ef58f | 2022.emnlp-main.744 | 2,022 | Providing context information from article and video titles improves model performance for offensiveness detection, target classification, and target group classification. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_6ec3577b0cfbf75c | 2022.emnlp-main.19 | 2,022 | MVQG is a dataset for generating engaging questions from multiple images. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_b4f074fe76cd2008 | 2022.emnlp-main.19 | 2,022 | Building stories behind an image sequence enables models to generate engaging questions. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_3849c91d5240f63f | 2024.emnlp-main.7 | 2,024 | A novel framework for Avalon gameplay features a multi-agent system that facilitates efficient communication and interaction among LLM-based agents. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_7165b67fa4639bdf | 2021.emnlp-main.651 | 2,021 | Humans are capable of generating a substantial amount of adversarial examples using semantics-preserving word substitutions. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_dca64e05ae788090 | 2021.emnlp-main.651 | 2,021 | Human-generated adversarial examples are not more able than the best algorithms to generate natural-reading, sentiment-preserving examples. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_caf5994597cd28ba | 2021.emnlp-main.651 | 2,021 | Human generation of adversarial examples is much more computationally efficient than the best algorithms. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_af2b75d5dc141f4e | 2020.emnlp-main.189 | 2,020 | BLANC uses context prediction as an auxiliary task in multi-task learning. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_88680137244093c8 | 2020.emnlp-main.189 | 2,020 | BLANC uses a block attention method to learn the context prediction task. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_4c32b3ac2670a960 | 2020.emnlp-main.189 | 2,020 | BLANC outperforms state-of-the-art QA models in reading comprehension, and this performance advantage increases as the number of answer text occurrences in the passage increases. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_c95f4c5519c5e5b3 | 2020.emnlp-main.189 | 2,020 | BLANC outperforms baseline models in a zero-shot setting for supporting fact prediction on HotpotQA after training on SQuAD. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_9c7483256ff7bb28 | 2023.emnlp-main.501 | 2,023 | Dialogue-guided Chain-of-Thought (DialCoT) decomposes complex reasoning problems into simpler sub-questions and generates intermediate reasoning steps in a dialogue format for Smaller Language Models. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_8eb920dadef4060d | 2023.emnlp-main.501 | 2,023 | Proximal Policy Optimization optimizes Smaller Language Models to select the optimal reasoning path. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_9ea9b18c143b9de6 | 2025.emnlp-main.355 | 2,025 | DICE uses small language models to refine large language model outputs via chain-of-thought correction. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f514cf53799d609d | 2025.emnlp-main.355 | 2,025 | DICE decouples reasoning and output specification adherence by first generating natural language responses with large language models and then refining them with small language models. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_c949a70b83ca2025 | 2025.emnlp-main.355 | 2,025 | DICE constructs structured chain-of-thought adaptation datasets using a two-stage method. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_606559b07ceb4428 | 2025.emnlp-main.355 | 2,025 | DICE applies a dual-tuning strategy to fine-tune small language models for generating structured outputs in an analyze-then-answer pattern. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_a752ef7d35e8c73e | 2025.emnlp-main.355 | 2,025 | DICE achieves state-of-the-art performance in format accuracy and content correctness for structured reasoning tasks. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_d57b38afbe9d76dc | 2021.emnlp-main.576 | 2,021 | Visual text representations create continuous vocabularies for machine translation by processing visually rendered text with sliding windows. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_2b5fb705b23a1898 | 2021.emnlp-main.576 | 2,021 | Models using visual text representations achieve comparable performance to traditional text models on small and large machine translation datasets. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_d72774fd2e3ac477 | 2021.emnlp-main.576 | 2,021 | Models using visual text representations exhibit significant robustness to varied types of noise in machine translation. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_ef3d7ef6367c19ac | 2021.emnlp-main.17 | 2,021 | The partition filter network decomposes feature encoding for joint entity and relation extraction into partition and filter steps. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f6408a92486532fd | 2021.emnlp-main.17 | 2,021 | The partition filter network uses entity and relation gates to segment neurons into two task-specific partitions and one shared partition. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_da2a386416e73940 | 2021.emnlp-main.17 | 2,021 | The shared partition in the partition filter network represents inter-task information and is evenly shared between entity and relation tasks to ensure two-way interaction. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_7c58e1441280f3fa | 2021.emnlp-main.17 | 2,021 | The task-specific partitions in the partition filter network represent intra-task information and are formed through concerted efforts of both gates to make entity feature encoding and relation feature encoding dependent on each other. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_001a7a97ec7f951d | 2021.emnlp-main.17 | 2,021 | Relation prediction contributes non-negligibly to named entity prediction in joint entity and relation extraction. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_799fb2d9ee4a2fa4 | 2025.emnlp-main.1317 | 2,025 | Performance degradation in Multimodal Large Language Models from corrupted training data is largely reversible. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_0165a962c2f78955 | 2025.emnlp-main.1317 | 2,025 | Disabling a small subset of parameters in corrupted Multimodal Large Language Models almost fully restores performance. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_ec8546b4bd6a5a22 | 2025.emnlp-main.1317 | 2,025 | Corrupted Multimodal Large Language Models inherently differentiate between clean and corrupted samples, facilitating dataset cleaning without external intervention. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_7ec4e0776cd3ea12 | 2025.emnlp-main.1317 | 2,025 | A corruption-robust training paradigm mitigates the effects of corrupted data in Visual Instruction Tuning. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
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