Papers by Xinchi Chen
Convolutional Interaction Network for Natural Language Inference (D18-1)
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| Challenge: | Attention-based neural models have achieved great success in natural language inference (NLI). |
| Approach: | They propose a general model to capture the interaction between two sentences, which can be an alternative to the attention mechanism for NLI. |
| Outcome: | The proposed model can capture complex interactions on three large datasets. |
Hybrid Hierarchical Retrieval for Open-Domain Question Answering (2023.findings-acl)
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| Challenge: | Recent work shows that dense hierarchical retrieval (DHR) can outperform dense passage retrieval. |
| Approach: | They propose a framework that applies sparse, dense and a combination of them to document and passage retrieval. |
| Outcome: | The proposed framework can outperform dense hierarchical retrieval (DHR) and sparse retrievers (BM25) on open-domain question answering (ODQA) datasets with an average improvement of 4.69% on recall@100 over DHR. |
Contrastive Document Representation Learning with Graph Attention Networks (2021.findings-emnlp)
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| Challenge: | Existing methods for document representation learning are significantly affected by the scarcity of document-level data. |
| Approach: | They propose to use a graph attention network on top of the available pretrained Transformers model to learn document embeddings. |
| Outcome: | Empirically, the proposed approach is effective in document classification and document retrieval tasks. |
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner (2022.findings-naacl)
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Danilo Neves Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henghui Zhu, Xinchi Chen, Peng Xu, Zhiheng Huang, Andrew Arnold, Dan Roth
| Challenge: | Large language models have achieved high performance on various natural language benchmarks, but the explainability of their output remains elusive. |
| Approach: | They propose an architecture called iterative retrieval-generation reasoner that generates an entailment tree that explains a given hypothesis by using premises from C. |
| Outcome: | The proposed model outperforms existing benchmarks on premise retrieval and entailment tree generation with around 300% gain in overall correctness. |
Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks (D19-1)
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| Challenge: | State-of-the-art SRL models do not model non-local interaction between arguments . e.g., LSTMs do not allow for efficient inference . |
| Approach: | They propose a new approach to model interactions between arguments using capsule networks . they analyze errors in the refinement procedure by capturing intuition in a flexible way . |
| Outcome: | The proposed model outperforms the baseline model on all 7 languages and achieves state-of-the-art results on 5 languages including English. |
Improving Cross-task Generalization of Unified Table-to-text Models with Compositional Task Configurations (2023.findings-acl)
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Jifan Chen, Yuhao Zhang, Lan Liu, Rui Dong, Xinchi Chen, Patrick Ng, William Yang Wang, Zhiheng Huang
| Challenge: | Existing methods for multitask learning typically use a dataset name as input prefix, which limits the effectiveness of multitask training. |
| Approach: | They propose compositional task configurations, a set of prompts prepended to the encoder to improve cross-task generalization of unified models. |
| Outcome: | The proposed model outperforms the UnifiedSKG baseline by noticeable margins in both in-domain and zero-shot settings. |
Benchmarking Query-Conditioned Natural Language Inference (2025.findings-acl)
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| Challenge: | Query-conditioned natural language inference (QC-NLI) is a new approach to detect inconsistencies in large language models. |
| Approach: | They propose a task of Query-Conditioned Natural Language Inference to determine the semantic relationship between two documents conditioned on a query. |
| Outcome: | The proposed task is based on a query-conditioned natural language inference (QC-NLI) it is used to determine the relationship between the premise and hypothesis given a given query. |
Language Agnostic Multilingual Information Retrieval with Contrastive Learning (2023.findings-acl)
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| Challenge: | Annotated training data is costly to obtain in many languages . |
| Approach: | They propose a semantic contrastive loss to align parallel sentences that share the same semantics in different languages and a language contrastive gain to leverage parallel sentence pairs to remove language-specific information from non-parallel corpora. |
| Outcome: | The proposed model improves retrieval performance while requiring less computational effort. |