Papers with natural language processing task
Parameter-Efficient Conversational Recommender System as a Language Processing Task (2024.eacl-long)
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| Challenge: | Existing methods to recommend items are categorized into attribute-based and generation-based methods. |
| Approach: | They propose to represent items in natural language and formulate a conversational recommender system that can be optimized in a single stage without relying on non-textual metadata. |
| Outcome: | The proposed model can be optimized in a single stage, without relying on non-textual metadata such as a knowledge graph. |
Temporal Validity Change Prediction (2024.findings-acl)
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| Challenge: | Existing benchmarking tasks require models to identify temporal validity duration of a single statement . however, many data sources contain additional context, which may alter the duration of the original statement if the context is present . |
| Approach: | They propose a task benchmarking the ability of machine learning to detect context statements that induce such change. |
| Outcome: | The proposed task uses a dataset of temporal target statements and crowdsource corresponding context statements to benchmark them. |
Temporal Relation Classification using Boolean Question Answering (2023.findings-acl)
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| Challenge: | a new approach for temporal relation classification (TRC) is proposed . a boolean question answering model is used to classify temporal relations between two events . |
| Approach: | They propose an efficient approach for temporal relation classification using a boolean question answering model based on TRC annotation guidelines. |
| Outcome: | The proposed model outperforms state-of-the-art models by 2.4% on questions designed by human annotation experts. |
Improving Relation Extraction through Syntax-induced Pre-training with Dependency Masking (2022.findings-acl)
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| Challenge: | Existing studies require modifications to existing baseline architectures to leverage syntactic information. |
| Approach: | They propose to leverage syntactic information to improve relation extraction by training a syntax-induced encoder on auto-parsed data through dependency masking. |
| Outcome: | The proposed approach outperforms baseline models and achieves state-of-the-art results on two English datasets. |
Try to Substitute: An Unsupervised Chinese Word Sense Disambiguation Method Based on HowNet (2020.coling-main)
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| Challenge: | Existing unsupervised methods for word sense disambiguation cannot work for HowNet-based WSD because of its uniqueness. |
| Approach: | They propose a method which exploits the masked language model task of pre-trained language models to conduct word sense disambiguation using a lexical knowledge base as the sense inventory. |
| Outcome: | The proposed method achieves significantly better performance than baseline methods. |
Zero-Shot Event Detection Based on Ordered Contrastive Learning and Prompt-Based Prediction (2022.findings-naacl)
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| Challenge: | Existing zero-shot event detection methods do not work for unseen types . supervised methods require predefined event types or external tools . |
| Approach: | They propose a framework to detect events from unstructured text without annotating samples . they propose to use ordered contrastive learning and prompt-based prediction to identify trigger words . |
| Outcome: | The proposed model detects events more effectively and accurately than state-of-the-art methods. |
Nested Named Entity Recognition as Corpus Aware Holistic Structure Parsing (2022.coling-1)
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| Challenge: | Named entity recognition is a natural language processing task . nested NER is based on a linear structure, but there is no research on applying corpus-level information to NER. |
| Approach: | They propose a holistic structure parsing algorithm to reveal the entire NEs in a sentence . they introduce points-wise mutual information and other frequency features from corpus-aware statistics . |
| Outcome: | The proposed model outperforms existing models on widely-used benchmarks and achieves state-of-the-art. |
Point to the Expression: Solving Algebraic Word Problems using the Expression-Pointer Transformer Model (2020.emnlp-main)
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| Challenge: | Existing models that generate solution equations using ‘Op (operator/operand) tokens suffered expression fragmentation and operand-context separation. |
| Approach: | They propose a pure neural model, Expression-Pointer Transformer, which uses (1) ‘Expression’ token and (2) operand-context pointers when generating solution equations. |
| Outcome: | The proposed model achieves comparable performance accuracy to state-of-the-art models and achieves better performance than existing models by at most 40%. |
An End-to-End Generative Architecture for Paraphrase Generation (D19-1)
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| Challenge: | Existing methods for generating paraphrases with linguistic knowledge are often domain specific and hard to scale, or yield inferior results. |
| Approach: | They propose an end-to-end conditional generative architecture for generating paraphrases via adversarial training which does not depend on extra linguistic information. |
| Outcome: | The proposed method outperforms existing models on automatic metrics and human evaluations on four public datasets. |
Hype or not? Formalizing Automatic Promotional Language Detection in Biomedical Research (2026.eacl-long)
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| Challenge: | Promotional language is a term used to undermine objective evaluation of evidence, impede research development, and erode trust in science. |
| Approach: | They propose formalized guidelines for identifying hype language and apply them to annotate a portion of the National Institutes of Health grant application corpus. |
| Outcome: | The proposed guidelines can help humans reliably annotate candidate hype adjectives and train machine learning models yield promising results. |
CILex: An Investigation of Context Information for Lexical Substitution Methods (2022.coling-1)
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| Challenge: | Existing methods for lexical substitution rely on manually curated lexicals and contextual word embedding models. |
| Approach: | They propose a method that uses contextual sentence embeddings to generate substitutes for a target word given a context and a model that captures additional context information complimenting contextual word embedders. |
| Outcome: | The proposed method is state-of-the-art on the widely used LS07 and CoInCo datasets with P@1 scores of 55.96% and 57.25% for lexical substitution. |
Improving Relation Extraction by Sequence-to-sequence-based Dependency Parsing Pre-training (2025.coling-main)
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| Challenge: | Existing studies show that dependency information is used only for encoder-only-based relation extraction tasks. |
| Approach: | They propose a syntax-aware seq2seq pre-trained model for relation extraction that incorporates dependency information into a seq2-trained language model by continual pre-training with a dependency parsing task. |
| Outcome: | The proposed model incorporates dependency information into a seq2seq pre-trained language model by continual pre-training with a generative sequence-to-sequence (sequ2sq)-based dependency parsing task. |
Enhancing Structure-aware Encoder with Extremely Limited Data for Graph-based Dependency Parsing (2022.coling-1)
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| Challenge: | Dependency parsing is an important natural language processing task which analyzes the syntactic structure of an input sentence. |
| Approach: | They propose a structure-aware encoder pre-trained on auto-parsed data to improve dependency parsing . they propose combining gold dependency trees with existing parsers to improve parser performance . |
| Outcome: | The proposed approach outperforms baselines under different parsers and dependency standards under different parameters and model architectures. |
Background Summarization of Event Timelines (2023.emnlp-main)
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| Challenge: | Generating concise summaries of news events is a challenging task for newcomers to a news story. |
| Approach: | They propose a task of background news summarization that complements each timeline update with a background summary of relevant preceding events. |
| Outcome: | The proposed system performs well on a question-answering-based evaluation metric, Background Utility Score (BUS). |
FactEval: Evaluating the Robustness of Fact Verification Systems in the Era of Large Language Models (2025.naacl-long)
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| Challenge: | Large language models (LLMs) have made significant advances in every natural language processing task, but they are vulnerable to small perturbations in the inputs, raising concerns about their robustness in the real world. |
| Approach: | They propose a large-scale benchmark for extensive evaluation of LLMs in the fact verification domain covering 17 realistic word-level and character-level perturbations and 4 types of subpopulations. |
| Outcome: | The proposed model is brittle to small input changes and exhibits performance variations across different subpopulations. |
SOUL: Towards Sentiment and Opinion Understanding of Language (2023.emnlp-main)
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| Challenge: | Sentiment analysis models often fail to capture the broader complexities of sentiment analysis. |
| Approach: | They propose a task to evaluate sentiment understanding through two subtasks . they annotate a new dataset comprising 15,028 statements from 3,638 reviews . |
| Outcome: | The proposed task evaluates sentiment understanding through two subtasks . it is a challenging task for both small and large language models, with performance gaps of up to 27% . |
Argument mining as a multi-hop generative machine reading comprehension task (2023.findings-emnlp)
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| Challenge: | Argument mining is a natural language processing task that aims to generate an argumentative graph given an unstructured argumentative text. |
| Approach: | They propose a new approach which transfers the argument mining task into a multi-hop reading comprehension task by incorporating a "chain of thought" information into the model. |
| Outcome: | The proposed approach surpasses SOTA results on two arguments mining benchmarks. |
Task-Aware Self-Supervised Framework for Dialogue Discourse Parsing (2023.findings-emnlp)
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| Challenge: | Existing discourse parsing approaches are constrained by predefined relation types, which can impede the adaptability of the parser for downstream tasks. |
| Approach: | They propose to introduce a task-aware paradigm to improve the versatility of the parser. |
| Outcome: | Empirical studies on dialogue discourse parsing datasets and a downstream task demonstrate the proposed framework. |