Papers by Xinyin Ma
SynET: Synonym Expansion using Transitivity (2020.findings-emnlp)
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| Challenge: | Existing approaches to find synonyms from text corpora are distributed and pattern based, but they suffer from low precision and low recall. |
| Approach: | They propose a task of synonym expansion using transitivity and propose auxiliary task to reduce the impact of noisy sentences. |
| Outcome: | The proposed approach reduces the impact of noisy sentences and reduces noise in a real-world dataset. |
CoT-Valve: Length-Compressible Chain-of-Thought Tuning (2025.acl-long)
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| Challenge: | Wei et al., 2022) have developed a powerful method for enhancing the reasoning capabilities of large language models. |
| Approach: | They propose to use a tuning and inference strategy to control the length of reasoning chains by a parameter space direction to control their length. |
| Outcome: | The proposed method reduces reasoning chains on GSM8K from 741 to 225 tokens with a minor performance drop (95.07% to 94.92%) and on AIME from 6827 to 4629 tokens, with only one additional incorrect answer. |
MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations (2021.emnlp-main)
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| Challenge: | Recent advances in entity retrieval ignore the property that meanings of entity mentions diverge in different contexts and are related to various portions of descriptions. |
| Approach: | They propose a novel approach that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method. |
| Outcome: | The proposed approach achieves state-of-the-art performance on ZESHEL and improves quality of candidates on three standard Entity Linking datasets. |
Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition (2021.acl-long)
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| Challenge: | Named entity recognition (NER) is a well-studied task in natural language processing. |
| Approach: | They propose a method that generates span proposals and labels them with categories . they use boundary information of entities and partially matched spans to locate them . |
| Outcome: | The proposed method outperforms state-of-the-art models on nested NER datasets. |
Adversarial Self-Supervised Data-Free Distillation for Text Classification (2020.emnlp-main)
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| Challenge: | Existing knowledge distillation algorithms rely on the accessibility of the training dataset, which may be unavailable due to privacy issues. |
| Approach: | They propose a data-free distillation method for a pre-trained transformer-based model that uses plug & play Embedding Guessing to craft pseudo embeddings from the teacher's hidden knowledge. |
| Outcome: | The proposed method is the first data-free distillation framework designed for NLP tasks. |