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
value | run_id large_stringclasses 2
values | created_at large_stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|
clm_0dedcbb2666f727e | 2025.emnlp-main.969 | 2,025 | The benchmark for solving linguistic puzzles is extended to include Linguistic Olympiad puzzles. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_b9a0f63cf2525b1d | 2025.emnlp-main.969 | 2,025 | Large Language Models outperform humans on most types of linguistic puzzles, except for puzzles centered on writing systems and puzzles involving understudied languages. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_fb457533d5e584f2 | 2020.emnlp-main.600 | 2,020 | Data augmentation for active learning uses labeled guided outputs from a language generation model to train text classifiers. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_82ce7b5c330aee3d | 2020.emnlp-main.600 | 2,020 | Text generation for data augmentation is optimized using Monte Carlo Tree Search to maximize output usefulness with entropy as an optimization criterion. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_829e92d79d0662e5 | 2020.emnlp-main.600 | 2,020 | Guided text generation with Monte Carlo Tree Search improves text classification performance on the TREC-6 and SST-2 datasets compared to non-guided data generation. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_931c9b1b4d05a2e2 | 2021.emnlp-main.539 | 2,021 | Levenshtein Training uses the Levenshtein Transformer for word-level quality estimation. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_783e2e6ca5e381a3 | 2021.emnlp-main.539 | 2,021 | A two-stage transfer learning procedure that uses augmented data and human post-editing data reduces the mismatch between machine translation and word-level quality estimation tasks. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_81c8e4d7e2a1092f | 2021.emnlp-main.539 | 2,021 | Heuristics for word-level quality estimation construct reference labels compatible with subword-level finetuning and inference. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f3acb7e23cc4a09b | 2021.emnlp-main.539 | 2,021 | Levenshtein Training has superior data efficiency for word-level quality estimation in data-constrained settings. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_4545aa61f2031441 | 2021.emnlp-main.539 | 2,021 | Levenshtein Training achieves competitive performance for word-level quality estimation in unconstrained settings. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_68b67acc19207263 | 2023.emnlp-main.803 | 2,023 | Imbalanced label distribution in question answering causes model collapse during test-time adaptation. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_039f0b619b75e7df | 2023.emnlp-main.803 | 2,023 | Anti-CF utilizes the source model's output to regularize the update of the adapted model during test time. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_5f4457444bd0a71c | 2023.emnlp-main.803 | 2,023 | Anti-CF includes an efficient side block that reduces inference time. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_6fc72ea382dcc4ce | 2023.emnlp-main.803 | 2,023 | Anti-CF achieves comparable or better results than previous test-time adaptation methods. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_380f3c3c92cd2f6a | 2023.emnlp-main.803 | 2,023 | Anti-CF maintains inference speed close to that of vanilla forward propagation. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_662e788aa6cca767 | 2022.emnlp-main.85 | 2,022 | Fine-grained Category Discovery under Coarse-grained supervision (FCDC) discovers fine-grained categories using only coarse-grained labeled data. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_5a7e0562b951193c | 2022.emnlp-main.85 | 2,022 | Hierarchical weighted self-contrastive learning combines a weighted self-contrastive module with supervised learning in a hierarchical manner to discover fine-grained categories from coarse-grained labeled data. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_4ae764b646e211fd | 2025.emnlp-main.1041 | 2,025 | Modern large language models can be effectively adapted for information reranking using minimal, high-quality supervision. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_8214864722e662f8 | 2025.emnlp-main.1041 | 2,025 | LIMRANK-SYNTHESIZER generates diverse, challenging, and realistic reranking examples. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_6eb6204a648e4e24 | 2025.emnlp-main.1041 | 2,025 | LIMRANK is a reranker model for reasoning-intensive information reranking. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_54427430bb939265 | 2025.emnlp-main.1041 | 2,025 | LIMRANK achieves competitive performance for information reranking with minimal training data. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_6f1573f04f349d27 | 2025.emnlp-main.1041 | 2,025 | LIMRANK generalizes to downstream tasks including scientific literature search and retrieval-augmented generation for knowledge-intensive problem solving. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_52fa922087765119 | 2023.emnlp-main.631 | 2,023 | The information gap between dialogue contexts and desired inferences renders the inductive inference process more challenging. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_69b39ac367212260 | 2023.emnlp-main.631 | 2,023 | Negative samples enable dialogue models to identify incorrect inferences. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_374f87805e5adda7 | 2021.emnlp-main.695 | 2,021 | A new Logical Form grammar models a wide range of knowledge graph queries and is sufficiently simple for efficient supervision data generation. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_05f2129942ddac21 | 2021.emnlp-main.695 | 2,021 | A Transformer-based model incorporates knowledge graph and conversational contexts via a JSON-like structured input, which is transformed to embeddings and processed by standard attention layers. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_eeb014e2fb61fb98 | 2021.emnlp-main.695 | 2,021 | A conversational question answering system over knowledge graphs with high-coverage grammar and structured context achieves higher grammar coverage and logical form execution accuracy on the CSQA dataset than prior work. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_0322ef7023fa3ed3 | 2022.emnlp-main.483 | 2,022 | RLET performs iterative single step reasoning for entailment tree generation using sentence selection and deduction generation modules. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_a690cf4da900a08e | 2022.emnlp-main.483 | 2,022 | RLET accumulates training signals across the entailment tree using an aligned reward function consistent with the evaluation. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_bfd91a275985fef4 | 2022.emnlp-main.483 | 2,022 | Reinforcement learning is introduced for entailment tree generation for the first time. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_458cf63c08b05381 | 2023.emnlp-main.491 | 2,023 | A systematic evaluation method assesses multilingual capabilities of large language models using qualitative and quantitative approaches. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_789a621bc3689275 | 2023.emnlp-main.491 | 2,023 | A novel prompt back-translation method evaluates cross-language generalization in large language models. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_62828758586eb499 | 2023.emnlp-main.491 | 2,023 | Large language models effectively transfer learned knowledge across different languages in tasks where the correct output is independent of the input language. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_007abdafee71c841 | 2023.emnlp-main.491 | 2,023 | Large language models struggle to provide accurate results in tasks where the correct output depends on the input language. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_42e38c535fafc291 | 2025.emnlp-main.590 | 2,025 | CEMTM infers coherent and interpretable topic structures from short and long documents containing text and images. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_791cbfb4a4dd33a7 | 2025.emnlp-main.590 | 2,025 | CEMTM uses fine-tuned large vision language models to obtain contextualized embeddings. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_d0735c405d72c58b | 2025.emnlp-main.590 | 2,025 | CEMTM employs a distributional attention mechanism to weight token-level contributions to topic inference. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_7ef321ff6bb00d54 | 2025.emnlp-main.590 | 2,025 | CEMTM processes multiple images per document without repeated encoding. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_0bbe5dab51790b1b | 2025.emnlp-main.590 | 2,025 | CEMTM captures visually grounded semantics in complex domains such as scientific articles. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_603d005331f18e9f | 2021.emnlp-main.101 | 2,021 | MS-Mentions provides entity mention annotations for Material Science synthesis procedural texts. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f1893129e0ad3067 | 2021.emnlp-main.101 | 2,021 | A new label inventory is designed to provide consistent entity mention annotations for Material Science synthesis procedural texts. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_d4c186375c07a46b | 2021.emnlp-main.101 | 2,021 | A new annotation approach is designed to maximize consistency and annotation speed for domain experts annotating Material Science synthesis procedural texts. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_efd6fc90606e9b2c | 2021.emnlp-main.101 | 2,021 | MS-Mentions entity mention annotations achieve high inter-annotator agreement. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_39465c10b97a7732 | 2021.emnlp-main.233 | 2,021 | Lifelong Explanation (LLE) continuously trains a student explainer under the supervision of a teacher explanation algorithm on different tasks in lifelong learning. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_9737005cfa7f7438 | 2021.emnlp-main.233 | 2,021 | Lifelong Explanation (LLE) uses Experience Replay to prevent catastrophic forgetting in the student explainer. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_7adeced41cc31ef4 | 2021.emnlp-main.233 | 2,021 | Lifelong Explanation (LLE) enhances the stability of explanations for all seen tasks. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_2d1194454904950d | 2021.emnlp-main.233 | 2,021 | Lifelong Explanation (LLE) maintains the same level of faithfulness to the black-box model as the teacher explanation algorithm. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_d158810991f6e3bf | 2021.emnlp-main.233 | 2,021 | Lifelong Explanation (LLE) is faster at test time than existing explanation methods. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_8003d20814746e79 | 2021.emnlp-main.233 | 2,021 | The Experience Replay mechanism in Lifelong Explanation (LLE) enhances the learning capabilities of the student explainer. | 5 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_02c8ab5f0243dd3f | 2021.emnlp-main.264 | 2,021 | Scheduled sampling based on decoding steps increases the selection chance of predicted tokens as decoding steps increase. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_92af2eb971b45bdc | 2021.emnlp-main.264 | 2,021 | Scheduled sampling based on decoding steps more realistically simulates the inference scene during training. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_09f7451675758c94 | 2021.emnlp-main.264 | 2,021 | Scheduled sampling based on both training steps and decoding steps improves neural machine translation performance. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_e847e05855e6dd78 | 2021.emnlp-main.264 | 2,021 | Scheduled sampling based on decoding steps outperforms the Transformer baseline and vanilla scheduled sampling on neural machine translation tasks. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_ffca3f16ff9c04b7 | 2021.emnlp-main.264 | 2,021 | Scheduled sampling based on decoding steps generalizes to text summarization tasks. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_e948d30e25f61afa | 2024.emnlp-main.72 | 2,024 | The predictive preferences of large language models are highly brittle and skewed even with semantically equivalent instructions. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_7c2110dd151f38cd | 2024.emnlp-main.72 | 2,024 | Fairer predictive preferences from large language models lead to judgments that are better aligned with humans. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_21d02f23498289e7 | 2024.emnlp-main.72 | 2,024 | ZEPO produces fairer preference decisions and improves the alignment of large language model evaluators with human judgments without requiring labeled data. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_c8a08d6072c40922 | 2024.emnlp-main.72 | 2,024 | ZEPO uses a zero-shot learning objective based on preference decision fairness. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_9394ecabeba37e0b | 2021.emnlp-main.57 | 2,021 | Training with gold AMR and machine-translated sentences improves multilingual AMR-to-text generation over training with generated silver AMR. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_254e44ab1641bf16 | 2021.emnlp-main.57 | 2,021 | Training with a combination of gold AMR with machine-translated sentences and generated silver AMR improves multilingual AMR-to-text generation. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_9a81ab0137b0b9b6 | 2021.emnlp-main.57 | 2,021 | Training with a combination of gold AMR with machine-translated sentences and generated silver AMR sets a new state of the art for multilingual AMR-to-text generation in German, Italian, Spain, and Chinese. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_c532a92ace58cbb6 | 2021.emnlp-main.199 | 2,021 | ConRPG uses a context regularizer for meaning in paraphrase generation. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_9c2ffbd7513906c1 | 2021.emnlp-main.199 | 2,021 | ConRPG generates massive amounts of high-quality paraphrase pairs. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_3bf7ad3a5eada2af | 2021.emnlp-main.199 | 2,021 | ConRPG produces paraphrases with more lexical and syntactic diversity. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_a85a5ee91cd2db51 | 2021.emnlp-main.199 | 2,021 | ConRPG allows developer intervention in the paraphrase generation process through human-interpretable scoring functions, resulting in a more controllable model. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_1a2c56cc00c816d1 | 2021.emnlp-main.199 | 2,021 | ConRPG outperforms existing paraphrase generation methods in both supervised and unsupervised setups. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_7e7e82fe449397f0 | 2020.emnlp-main.132 | 2,020 | A pre-trained encoder for entity relation extraction uses a span encoder and a span pair encoder to incorporate intra-span and inter-span information. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_483c129cab257028 | 2020.emnlp-main.132 | 2,020 | A span encoder recovers the original token order within a span from a randomly shuffled sequence. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_880c8b962f08d0bb | 2020.emnlp-main.132 | 2,020 | A span pair encoder distinguishes span pairs from the same sentence as positive and from different sentences as negative using contrastive loss. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_472c036d5a42b63e | 2020.emnlp-main.132 | 2,020 | In entity relation extraction, pre-training with intra-span and inter-span information outperforms distantly supervised pre-training. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_3526133d46d8c23d | 2021.emnlp-main.729 | 2,021 | A collaborative learning framework for unsupervised text style transfer uses a pair of decoders with opposite decoding directions. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_6dc668ebf9a0cfe1 | 2021.emnlp-main.729 | 2,021 | A collaborative learning framework for unsupervised text style transfer regularizes each decoder by knowledge from the peer decoder. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_0df1a7f0793efd0e | 2021.emnlp-main.729 | 2,021 | A collaborative learning framework for unsupervised text style transfer guarantees the difference in knowledge acquisition between decoders by opposite decoding directions and a distinguishability constraint. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_8d5f3747a9e51b59 | 2021.emnlp-main.729 | 2,021 | A collaborative learning framework for unsupervised text style transfer employs mutual knowledge distillation to drive both decoders to a better optimum and alleviate the over-transfer and under-transfer problems. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f255826145635a2d | 2020.emnlp-main.403 | 2,020 | NSP is detrimental to BERT pre-training due to context splitting and shallow semantic signal. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_ff2b30c11fbc660d | 2020.emnlp-main.403 | 2,020 | Sentence ordering, adjacent sentence prediction, TF prediction, TF-IDF prediction, a FastSent variant, and a Quick Thoughts variant yield better performance than pure masked language modeling when used as auxiliary pre-training tasks. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_c067ed292d178415 | 2020.emnlp-main.403 | 2,020 | Multi-task pre-training with multiple auxiliary tasks yields better model performance than pre-training with any single auxiliary task. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_bc3080acdd1a5433 | 2020.emnlp-main.403 | 2,020 | A language model pre-trained with multiple auxiliary tasks outperforms BERTBase on the GLUE benchmark with substantially reduced training data. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_522c41178195c2dc | 2020.emnlp-main.736 | 2,020 | UNION evaluates the quality of generated stories in open-ended story generation without references. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_8594c78774e7cd7b | 2020.emnlp-main.736 | 2,020 | UNION distinguishes human-written stories from negative samples. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_1804f4f6f058eec2 | 2020.emnlp-main.736 | 2,020 | UNION recovers the perturbation in negative stories. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_c29a7e2e1e7d238d | 2020.emnlp-main.736 | 2,020 | Negative samples for open-ended story generation evaluation mimic repeated plots, conflicting logic, and long-range incoherence errors from existing NLG models. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_2098941135495550 | 2020.emnlp-main.736 | 2,020 | UNION correlates better with human judgments for open-ended story generation evaluation than existing state-of-the-art metrics. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_2c302c4fb3a2e249 | 2020.emnlp-main.736 | 2,020 | UNION is more generalizable for open-ended story generation evaluation than existing state-of-the-art metrics. | 5 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_8eb16e538660576f | 2024.emnlp-main.962 | 2,024 | MedCoT performs Medical Visual Question Answering using a hierarchical chain of specialists where an Initial Specialist proposes diagnostic rationales, a Follow-up Specialist validates these rationales, and a sparse Mixture of Experts within a Diagnostic Specialist reaches consensus through a vote for definitive diagno... | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_79caeb009f7f84a3 | 2024.emnlp-main.962 | 2,024 | MedCoT improves performance and interpretability in Medical Visual Question Answering. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_4a736f6adb30e922 | 2022.emnlp-main.481 | 2,022 | FLUTE is a dataset of figurative language natural language inference instances with textual explanations for Sarcasm, Simile, Metaphor, and Idioms. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_247e277c2d720ac6 | 2022.emnlp-main.481 | 2,022 | A Human-AI collaboration framework utilizing GPT-3 and human annotators (novices and experts) aids in scaling up the creation of datasets for complex linguistic phenomena such as figurative language. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_ee4dd43110927ca8 | 2023.emnlp-main.965 | 2,023 | Measuring variance of word vector distributions detects semantic differences in words appearing in two corpora without requiring corpus or word alignments. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_2bc0af6932ef790a | 2023.emnlp-main.965 | 2,023 | The norm of the mean word vector measures the coverage of meanings of a word in a corpus. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_dad8ca5220026455 | 2023.emnlp-main.965 | 2,023 | Measuring variance of word vector distributions is robust to corpus size skew. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_c288cd0d30533b06 | 2023.emnlp-main.965 | 2,023 | Measuring variance of word vector distributions detects semantic differences in infrequent words appearing in two corpora. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_c28e3ed02984611c | 2023.emnlp-main.965 | 2,023 | Measuring variance of word vector distributions pinpoints word instances with a meaning missing in one of two corpora. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f4cc23f133bcafea | 2024.emnlp-main.727 | 2,024 | Plan-guided retrieval uses the planning capabilities of instruction-tuned LLMs to guide retrieval for grounded text generation. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_a8c69e8912bf20b7 | 2024.emnlp-main.727 | 2,024 | Plan-guided retrieval improves the coverage of relevant facts in long-form text generation. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_4aba05fdcbc45860 | 2024.emnlp-main.727 | 2,024 | Plan-guided retrieval produces more informative responses in long-form text generation. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_3f497a291e78a48c | 2024.emnlp-main.727 | 2,024 | Plan-guided retrieval provides a higher rate of attribution to source documents in long-form text generation. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_946d84cb8d8362df | 2022.emnlp-main.70 | 2,022 | Authorship Representation Regularization (ARR) creates authorship representation with reduced reliance on topic-specific information. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_70ffc191b7923eec | 2022.emnlp-main.70 | 2,022 | A cross-topic-open-set evaluation method evaluates authorship representation performance for unseen authors and unseen topics. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_6e0b37a5496ca4ff | 2022.emnlp-main.262 | 2,022 | Few-shot hardness is intrinsic to datasets for a given pre-trained model. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.