Papers by Yuanhe Tian
Relation Extraction with Type-aware Map Memories of Word Dependencies (2021.findings-acl)
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| Challenge: | Existing studies focus on the dependency connections between words with limited attention paid to exploiting dependency types. |
| Approach: | They propose a neural approach for relation extraction with type-aware map memories . they map all associated words along with dependencies among them to memory slots . |
| Outcome: | The proposed approach achieves state-of-the-art on two English benchmark datasets. |
Improving Chinese Word Segmentation with Wordhood Memory Networks (2020.acl-main)
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| Challenge: | Contextual features are important in Chinese word segmentation (CWS) but it is difficult to integrate wordhood information into existing neural models. |
| Approach: | They propose a neural framework that integrates contextual wordhood information with several popular encoder-decoder combinations for Chinese word segmentation. |
| Outcome: | The proposed framework achieves state-of-the-art performance on five benchmark datasets. |
Aspect-based Sentiment Analysis with Type-aware Graph Convolutional Networks and Layer Ensemble (2021.naacl-main)
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| Challenge: | Existing studies only leverage dependency relations without considering their dependency types . a valid and effective approach is demonstrated on six English benchmark datasets . |
| Approach: | They propose to explicitly utilize dependency types for ABSA with type-aware graph convolutional networks . attention is used in T-GCN to distinguish different edges in the graph and attentive layer ensemble to comprehensively learn from different layers of T-gCN. |
| Outcome: | The proposed approach performs well on six English benchmark datasets. |
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. |
Enhancing Aspect-level Sentiment Analysis with Word Dependencies (2021.eacl-main)
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| Challenge: | Existing approaches to enhance aspect-level sentiment analysis have omitted syntactic information . experimental results show that our approach outperforms baseline models on all datasets . |
| Approach: | They propose to leverage word dependencies to enhance aspect-level sentiment analysis . they propose to use key-value memory networks to leverage different dependency results . |
| Outcome: | The proposed approach outperforms baseline models on all datasets and achieves state-of-the-art performance on three of them. |
Improving Image Captioning via Predicting Structured Concepts (2023.emnlp-main)
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| Challenge: | Existing studies on image captioning ignore the relationship between concepts . current methods for image caption generation ignore this relationship . |
| Approach: | They propose a structured concept predictor to predict concepts and their structures . they integrate these predictions into captioning to enhance visual signals . |
| Outcome: | The proposed approach improves image captioning performance by using semantic concepts as a bridge between images and texts. |
ChiMed-GPT: A Chinese Medical Large Language Model with Full Training Regime and Better Alignment to Human Preferences (2024.acl-long)
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| Challenge: | Current large language models (LLMs) are ineffective in learning domain knowledge and aligning with human preference. |
| Approach: | They propose a benchmark LLM for Chinese medical domain that uses pre-training, supervised fine-tuning and RLHF to train LLMs. |
| Outcome: | The proposed LLM performs better than existing LLMs in the Chinese medical domain. |
Syntax-driven Approach for Semantic Role Labeling (2022.lrec-1)
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| Challenge: | Existing studies focus on auto-generated syntactic knowledge to enhance semantic role labeling . experimental results show that map memories can enhance SRL . |
| Approach: | They propose to map memories to enhance semantic role labeling by encoding auto-generated syntactic knowledge from off-the-shelf toolkits. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art results on two English benchmark datasets. |
Combinatory Grammar Tells Underlying Relevance among Entities (2022.findings-emnlp)
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| Challenge: | Existing approaches focus on dependencies among words while paying limited attention to other types of syntactic structure. |
| Approach: | They propose an alternative approach that takes advantage of combinatory categorial grammar to detect the relation between entities. |
| Outcome: | The proposed model performs state-of-the-art on two widely used English benchmark datasets. |
End-to-end Aspect-based Sentiment Analysis with Combinatory Categorial Grammar (2023.findings-acl)
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| Challenge: | End-to-end aspect-based sentiment analysis (EASA) is a natural language processing task that requires a deep understanding of the running text. |
| Approach: | They propose a method to improve EASA with CCG supertags that carry syntactic and semantic information of the associated words. |
| Outcome: | The proposed approach outperforms baselines and achieves state-of-the-art results on all datasets. |
Improving Arabic Diacritization with Regularized Decoding and Adversarial Training (2021.acl-short)
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| Challenge: | Existing studies regard auto-generated knowledge instances as gold references, which limits their effectiveness since they are not always accurate and inferior instances can lead to incorrect predictions. |
| Approach: | They propose to use regularized decoding and adversarial training to appropriately learn from noisy knowledge instances for Arabic diacritization. |
| Outcome: | The proposed model outperforms existing models on two benchmark datasets even with flawed auto-generated knowledge. |
Improving English-Arabic Transliteration with Phonemic Memories (2022.findings-emnlp)
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| Challenge: | Existing neural approaches to transliterate names from English to Arabic are limited and focus on leveraging the phonemic association between English and Arabic. |
| Approach: | They propose a model for English-Arabic transliteration using a memory module modeling the phonemic association between English and Arabic to guide the transliterations process. |
| Outcome: | The proposed model improves on EANames corpus, which better represents names in the general public than linked Wikipedia entries that are always names of famous people. |
Named Entity Recognition for Social Media Texts with Semantic Augmentation (2020.emnlp-main)
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| Challenge: | Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts. |
| Approach: | They propose a neural-based approach to named entity recognition for social media texts . they obtain augmented semantic information from a large-scale corpus and encode it . |
| Outcome: | The proposed approach outperforms existing approaches on three social media datasets. |
Summarizing Medical Conversations via Identifying Important Utterances (2020.coling-main)
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| Challenge: | Applying natural language processing (NLP) techniques to the medical field is a prevailing trend nowadays and has great potential in many applications, such as key information extraction in medical literature. |
| Approach: | They propose to use a hierarchical encoder-tagger model to generate medical conversation summarization by identifying important utterances. |
| Outcome: | The proposed model outperforms baseline models and models and adds conversation-related features to improve performance. |
Relation Extraction with Word Graphs from N-grams (2021.emnlp-main)
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| Challenge: | Recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to improve performance. |
| Approach: | They propose to use a graph convolutional network to build a context graph without dependency parsers. |
| Outcome: | The proposed approach improves neural RE methods without dependency parsers on English benchmark datasets. |
SEE: Signal Embedding Energy for Quantifying Noise Interference in Large Audio Language Models (2026.acl-long)
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| Challenge: | Existing studies on noise lack quantitative analysis and rely on intuition and empirical observation, thus failing to understand practical robustness. |
| Approach: | They propose a method for quantifying the impact of noise intensity on LALM inputs by using a structured activation subspace derived from the model's internal representations. |
| Outcome: | The proposed method outperforms existing denoising methods and demonstrates that noise is perceived more accurately than raw audio features. |
Enhancing Relation Extraction via Adversarial Multi-task Learning (2022.lrec-1)
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| Challenge: | Existing studies have focused on re-modeling the given NEs and thus lead to inferior results when NE is sometimes ambiguous. |
| Approach: | They propose a relation extraction model with two training stages that uses adversarial multi-task learning to recover the given NEs. |
| Outcome: | The proposed model improves on two English benchmark datasets and shows state-of-the-art performance. |
Supertagging Combinatory Categorial Grammar with Attentive Graph Convolutional Networks (2020.emnlp-main)
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| Challenge: | Existing studies have focused on supertagging but have not tapped into contextual information. |
| Approach: | They propose to build a graph from chunks extracted from a lexicon and apply attention over it to enhance supertagging by leveraging contextual information. |
| Outcome: | The proposed approach outperforms previous studies in terms of supertagging and parsing. |
Learning Multimodal Contrast with Cross-modal Memory and Reinforced Contrast Recognition (2024.findings-acl)
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| Challenge: | Using a memory module, we learn multimodal contrast using encoding-decoding paradigm . multimodal information are used in many applications, including news feeding, social media, etc. |
| Approach: | They propose an LLM-based approach for learning multimodal contrast following the encoding-decoding paradigm . they use a memory module with reinforced contrast recognition to enhance learning . |
| Outcome: | The proposed approach outperforms baseline and state-of-the-art studies on four English and Chinese benchmark datasets. |
Dialogue Summarization with Mixture of Experts based on Large Language Models (2024.acl-long)
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| Challenge: | Existing studies for dialogue summarization use one model at a time or treat it as a black box. |
| Approach: | They propose an LLM-based approach with role-oriented routing and fusion generation to utilize mixture of experts for dialogue summarization. |
| Outcome: | The proposed approach produces informative and accurate dialogue summarization on widely used datasets. |
Joint Chinese Word Segmentation and Part-of-speech Tagging via Multi-channel Attention of Character N-grams (2020.coling-main)
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| Challenge: | Chinese word segmentation and part-of-speech tagging can be performed in a sequential order . existing studies have shown that jointly performing them can be effective . |
| Approach: | They propose a character-based neural model enhanced by multi-channel attention of n-grams. |
| Outcome: | The proposed model outperforms baseline models on five benchmark datasets. |
Improving Constituency Parsing with Span Attention (2020.findings-emnlp)
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| Challenge: | Constituency parsing is a fundamental task for natural language understanding . n-grams are a conventional type of feature for contextual information . experimental results show that neural parsers with no grammar rules outperform statistical ones . |
| Approach: | They propose to incorporate n-grams into span representations by weighting them according to their contributions to the parsing process. |
| Outcome: | The proposed approach outperforms existing statistical grammar-based models on Arabic, Chinese, and English datasets. |
Challenging Large Language Models with New Tasks: A Study on their Adaptability and Robustness (2024.findings-acl)
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| Challenge: | Existing evaluation approaches for large language models (LLMs) rely on existing tasks and benchmarks, raising concerns about test set contamination and the genuine comprehension abilities of LLMs. |
| Approach: | They propose to evaluate LLMs by designing new tasks, automatically generating evaluation datasets for the tasks, and conducting detailed error analyses to scrutinize LLM's adaptability to new tasks. |
| Outcome: | The proposed method examines LLMs’ adaptability to new tasks, their sensitivity to prompt variations, and their error tendencies. |
RESEMO: A Benchmark Chinese Dataset for Studying Responsive Emotion from Social Media Content (2024.findings-acl)
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| Challenge: | Existing studies on social media text processing do not focus on responsive emotion analysis. |
| Approach: | They propose a Chinese dataset named ResEmo for responsive emotion analysis, including 3813 posts with 68,781 comments collected from Weibo, the largest social media platform in China. |
| Outcome: | The proposed dataset includes 3813 posts with 68,781 comments collected from weibo, the largest social media platform in China. |
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. |
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories (2021.emnlp-main)
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| Challenge: | Aspect-based sentiment analysis (ABSA) predicts sentiment polarity for aspect term in sentences . labeled data stored at different locations and inaccessible due to privacy or legal concerns . |
| Approach: | They propose a model with federated learning to combine labeled data across different domains . they incorporate topic memory to take data from diverse domains into consideration . |
| Outcome: | The proposed model outperforms baselines on a simulated environment with three nodes. |
Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information (2020.findings-emnlp)
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| Challenge: | Existing studies have shown that named entity recognition (NER) is effective in encoding and aggregating syntactic information, but they lack the appropriate knowledge to model such properties. |
| Approach: | They propose to leverage syntactic information by leveraging attentive ensembles to model NER . they propose key-value memory networks, syntax attention and gate mechanism for encoding, weighting and aggregating syntaktic information. |
| Outcome: | The proposed model outperforms previous studies on six English and Chinese benchmark datasets. |
Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks (2020.coling-main)
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| Challenge: | End-to-end aspect-based sentiment analysis uses two sub-tasks to extract aspect terms . experimental results demonstrate the effectiveness of our approach on all datasets . |
| Approach: | They propose to combine aspect extraction and sentiment analysis with encoding syntactic information to improve model's representation of input sentences. |
| Outcome: | The proposed approach achieves state-of-the-art on three benchmark datasets. |
Aspect-based Sentiment Analysis with Context Denoising (2024.findings-naacl)
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| Challenge: | Existing approaches to ABSA use text encoders to locate important context features or remove them from input. |
| Approach: | They propose to improve ABSA with context denoising to remove noise from text . they use diffusion networks to perform denoizing process to gradually eliminate noise . paper shows that aspect-based sentiment analysis is effective for fine-grained analysis . |
| Outcome: | The proposed approach improves ABSA on five widely used ABSA datasets. |
ChiMST: A Chinese Medical Corpus for Word Segmentation and Medical Term Recognition (2022.lrec-1)
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| Challenge: | Chinese word segmentation and named entity recognition are important tasks in natural language processing. |
| Approach: | They develop a Chinese medical corpus annotated with Chinese word boundary and medical term information to address this problem. |
| Outcome: | The proposed corpus will be a valuable resource for Chinese word segmentation and named entity recognition research on the medical domain. |
Federated Chinese Word Segmentation with Global Character Associations (2021.findings-acl)
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| Challenge: | Chinese word segmentation (CWS) is a fundamental task for natural language processing. |
| Approach: | They propose a neural model for Chinese word segmentation with federated learning to help CWS deal with data isolation. |
| Outcome: | The proposed model outperforms baselines on a simulated environment with five nodes. |
Large Language Models Are No Longer Shallow Parsers (2024.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have reshaped the field of natural language processing (NLP) however, fundamental NLP tasks that involve linguistic analysis still play essential roles in the field. |
| Approach: | They propose to use constituency parsing to improve performance of LLMs on deep syntactic parse trees to prompt LLM chunking, filter out low-quality chunks and add remaining chunks to prompts to instruct LLM for parser. |
| Outcome: | The proposed approach improves LLMs' performance on constituency parsing on English and Chinese benchmark datasets. |
Complementary Learning of Aspect Terms for Aspect-based Sentiment Analysis (2022.lrec-1)
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| Challenge: | Existing ABSA models do not pay attention to aspect terms and their contexts . a discriminator is introduced to improve ABSA, allowing for better understanding of aspect terms . |
| Approach: | They propose to improve ABSA by complementary learning of aspect terms . they explicitly recover aspect terms from each input sentence to better understand aspects . |
| Outcome: | The proposed approach improves ABSA on five widely used English benchmark datasets. |
Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks (2021.acl-long)
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| Challenge: | Existing studies suffer from noise in dependency trees, which can cause confusions in relation extraction. |
| Approach: | They propose a dependency-driven approach for relation extraction with attentive graph convolutional networks . they apply an attention mechanism upon graph convolutional networks to different word dependencies . |
| Outcome: | The proposed approach outperforms previous studies on two English datasets and achieves state-of-the-art performance. |
Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge (2020.acl-main)
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| Challenge: | Chinese word segmentation and part-of-speech tagging are important fundamental tasks in natural language processing. |
| Approach: | They propose a neural model for Chinese word segmentation and part-of-speech tagging . they incorporate context features and syntactic knowledge for each input character . |
| Outcome: | The proposed model can learn and benefit from existing tools, but its quality may be poor. |