Papers by Jiyi Li
Exploiting Labeled and Unlabeled Data via Transformer Fine-tuning for Peer-Review Score Prediction (2022.findings-emnlp)
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| Challenge: | Existing work on automatic peer-review aspect score prediction rely on limited data sets. |
| Approach: | They propose a semi-supervised learning method that incorporates the Transformer fine-tuning into the -model to leverage contextual features from unlabeled data. |
| Outcome: | The proposed method outperforms supervised and naive methods in the peer-review dataset. |
AHP-Powered LLM Reasoning for Multi-Criteria Evaluation of Open-Ended Responses (2024.findings-emnlp)
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| Challenge: | Question answering (QA) tasks have been extensively studied in the field of natural language processing. |
| Approach: | They propose a method that leverages large language models and the analytic hierarchy process to assess open-ended questions. |
| Outcome: | The proposed method more closely aligns with human judgment compared to baselines on four datasets. |
Text Categorization by Learning Predominant Sense of Words as Auxiliary Task (P19-1)
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| Challenge: | Existing methods for text categorization use implicit representations to learn the senses of words. |
| Approach: | They propose a method for text categorization by leveraging the predominant sense of words depending on the domain. |
| Outcome: | The proposed model improves performance on four benchmark datasets. |
Adversarial Speech Generation and Natural Speech Recovery for Speech Content Protection (2022.lrec-1)
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| Challenge: | Currently, researchers focus on how to protect the speaker's identifiable information, represented as voiceprint, contained in the speech. |
| Approach: | They propose a frame-by-frame adversarial speech generation system to protect speech . they build an adversarials-based method that converts adversarially generated speech to human speech. |
| Outcome: | The proposed method can encode and recover any sensitive audio, and it is easy to be conducted with publicly available speech recognition technology. |
Evaluating Saliency Explanations in NLP by Crowdsourcing (2024.lrec-main)
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| Challenge: | a crowdsourced method to evaluate saliency methods in NLP is proposed . saliencies are difficult for humans to understand, and can cause psychological harm . |
| Approach: | They propose a method to evaluate saliency methods in NLP by crowdsourcing . they recruited 800 crowd workers and empirically evaluated seven salience methods . |
| Outcome: | The proposed method evaluates saliency methods on two datasets using crowdsourced data . it shows that the results are comparable to existing methods on NLP and CV fields . |
Multi-Domain Dialogue State Tracking with Disentangled Domain-Slot Attention (2023.findings-acl)
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| Challenge: | Multi-domain dialogue state tracking is a challenge for task-oriented dialogue systems . domains and slots are aggregated into a single query to generate domain-slot specific representations . |
| Approach: | They propose to disentangle domain-slot attention for multi-domain dialogue state tracking by separating query about domains and slots from the attention component. |
| Outcome: | The proposed approach outperforms the standard multi-head attention with aggregated domain-slot query. |
A Dataset of Crowdsourced Word Sequences: Collections and Answer Aggregation for Ground Truth Creation (D19-59)
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| Challenge: | Existing work on answer aggregation for labels is limited . existing work on label aggregations is limited to label . |
| Approach: | They propose three approaches to extractive word sequence aggregation from translated sentences generated by multiple workers. |
| Outcome: | The proposed dataset contains translated sentences generated from multiple workers. |
Aspect-Category Enhanced Learning with a Neural Coherence Model for Implicit Sentiment Analysis (2023.findings-emnlp)
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| Challenge: | Aspect-based sentiment analysis (ABSA) is a major research topic in NLP since social networking services have increased . but the recognition of implicit sentiments that do not contain obvious opinion words remains unexplored . elcom captures document-level coherence by using contrastive learning and sentence-level by a hypergraph . |
| Approach: | They propose aspect-category enhanced learning with a neural coherence model . it captures document-level coherency by contrastive learning and sentence-level by a hypergraph . |
| Outcome: | The proposed model captures document-level coherence by using contrastive learning and sentence-level by a hypergraph to mine opinions from explicit sentences to aid implicit sentiment classification. |
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization (D18-1)
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| Challenge: | Existing methods for categorization of short texts use non-hierarchical flat model, but they are limited by domain-independent knowledge distribution. |
| Approach: | They propose a method which leverages hierarchical relationships between pre-defined categories to tackle the data sparsity problem. |
| Outcome: | The proposed method is competitive with the state-of-the-art methods on a multi-label categorization task for short texts using two benchmark datasets. |
HSCNN: A Hybrid-Siamese Convolutional Neural Network for Extremely Imbalanced Multi-label Text Classification (2020.emnlp-main)
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| Challenge: | Existing approaches to solve the data imbalance problem are limited in extremely imbalanced data. |
| Approach: | They propose a hybrid approach which adapts general networks for head categories and few-shot techniques for tail categories. |
| Outcome: | The proposed approach improves the performance of Single networks with diverse loss objectives on tail or entire categories. |
A Neural Local Coherence Analysis Model for Clarity Text Scoring (2020.coling-main)
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| Challenge: | Existing methods for scoring text clarity use local coherence between adjacent sentences . local cohesion is one of the main properties to identify whether a text is well-structured or not. |
| Approach: | They propose a method for scoring text clarity by utilizing local coherence between adjacent sentences. |
| Outcome: | The proposed method improves on the PeerRead benchmark dataset. |
Enhanced Coherence-Aware Network with Hierarchical Disentanglement for Aspect-Category Sentiment Analysis (2024.lrec-main)
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| Challenge: | Aspect-category-based sentiment analysis (ACSA) is a popular approach for identifying aspect categories and predicting their sentiments. |
| Approach: | They propose an enhanced coherence-aware network with hierarchical disentanglement (ECAN) to capture contexts across the whole review and to help the implicit aspect and sentiment identification. |
| Outcome: | The proposed network decouples multiple aspects and sentiment features and achieves state-of-the-art (SOTA) performance. |
Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verification (2021.emnlp-main)
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| Challenge: | Existing scientific claim verification models have problems of error propagation among modules and lack of sharing valuable information among modules. |
| Approach: | They propose an approach that jointly learns the modules for the three tasks with a machine reading comprehension framework by including claim information. |
| Outcome: | The proposed approach outperforms existing models on the SciFact dataset on the three tasks of abstract retrieval, rationale selection and stance prediction. |
Human-LLM Hybrid Text Answer Aggregation for Crowd Annotations (2024.emnlp-main)
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| Challenge: | Existing studies on crowd text answer aggregation focus on individual crowd workers' average performance, but the role of LLMs as aggregators is not well-studied. |
| Approach: | They propose a human-LLM hybrid text answer aggregation method with a Creator-Aggregator Multi-Stage crowdsourcing framework. |
| Outcome: | The proposed method is based on a Creator-Aggregator Multi-Stage crowdsourcing framework. |