Yan Chen


2024

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Generalized Category Discovery with Large Language Models in the Loop
Wenbin An | Wenkai Shi | Feng Tian | Haonan Lin | QianYing Wang | Yaqiang Wu | Mingxiang Cai | Luyan Wang | Yan Chen | Haiping Zhu | Ping Chen
Findings of the Association for Computational Linguistics ACL 2024

Generalized Category Discovery (GCD) is a crucial task that aims to recognize both known and novel categories from a set of unlabeled data by utilizing a few labeled data with only known categories. Due to the lack of supervision and category information, current methods usually perform poorly on novel categories and struggle to reveal semantic meanings of the discovered clusters, which limits their applications in the real world. To mitigate the above issues, we propose Loop, an end-to-end active-learning framework that introduces Large Language Models (LLMs) into the training loop, which can boost model performance and generate category names without relying on any human efforts. Specifically, we first propose Local Inconsistent Sampling (LIS) to select samples that have a higher probability of falling to wrong clusters, based on neighborhood prediction consistency and entropy of cluster assignment probabilities. Then we propose a Scalable Query strategy to allow LLMs to choose true neighbors of the selected samples from multiple candidate samples. Based on the feedback from LLMs, we perform Refined Neighborhood Contrastive Learning (RNCL) to pull samples and their neighbors closer to learn clustering-friendly representations. Finally, we select representative samples from clusters corresponding to novel categories to allow LLMs to generate category names for them. Extensive experiments on three benchmark datasets show that Loop outperforms SOTA models by a large margin and generates accurate category names for the discovered clusters. Code and data are available at https://github.com/Lackel/LOOP.

2023

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DNA: Denoised Neighborhood Aggregation for Fine-grained Category Discovery
Wenbin An | Feng Tian | Wenkai Shi | Yan Chen | Qinghua Zheng | QianYing Wang | Ping Chen
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Discovering fine-grained categories from coarsely labeled data is a practical and challenging task, which can bridge the gap between the demand for fine-grained analysis and the high annotation cost. Previous works mainly focus on instance-level discrimination to learn low-level features, but ignore semantic similarities between data, which may prevent these models learning compact cluster representations. In this paper, we propose Denoised Neighborhood Aggregation (DNA), a self-supervised framework that encodes semantic structures of data into the embedding space. Specifically, we retrieve k-nearest neighbors of a query as its positive keys to capture semantic similarities between data and then aggregate information from the neighbors to learn compact cluster representations, which can make fine-grained categories more separatable. However, the retrieved neighbors can be noisy and contain many false-positive keys, which can degrade the quality of learned embeddings. To cope with this challenge, we propose three principles to filter out these false neighbors for better representation learning. Furthermore, we theoretically justify that the learning objective of our framework is equivalent to a clustering loss, which can capture semantic similarities between data to form compact fine-grained clusters. Extensive experiments on three benchmark datasets show that our method can retrieve more accurate neighbors (21.31% accuracy improvement) and outperform state-of-the-art models by a large margin (average 9.96% improvement on three metrics). Our code and data are available at https://github.com/Lackel/DNA.

2022

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Roadblocks in Gender Bias Measurement for Diachronic Corpora
Saied Alshahrani | Esma Wali | Abdullah R Alshamsan | Yan Chen | Jeanna Matthews
Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change

The use of word embeddings is an important NLP technique for extracting meaningful conclusions from corpora of human text. One important question that has been raised about word embeddings is the degree of gender bias learned from corpora. Bolukbasi et al. (2016) proposed an important technique for quantifying gender bias in word embeddings that, at its heart, is lexically based and relies on sets of highly gendered word pairs (e.g., mother/father and madam/sir) and a list of professions words (e.g., doctor and nurse). In this paper, we document problems that arise with this method to quantify gender bias in diachronic corpora. Focusing on Arabic and Chinese corpora, in particular, we document clear changes in profession words used over time and, somewhat surprisingly, even changes in the simpler gendered defining set word pairs. We further document complications in languages such as Arabic, where many words are highly polysemous/homonymous, especially female professions words.

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Towards Generalizeable Semantic Product Search by Text Similarity Pre-training on Search Click Logs
Zheng Liu | Wei Zhang | Yan Chen | Weiyi Sun | Tianchuan Du | Benjamin Schroeder
Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)

Recently, semantic search has been successfully applied to E-commerce product search and the learned semantic space for query and product encoding are expected to generalize well to unseen queries or products. Yet, whether generalization can conveniently emerge has not been thoroughly studied in the domain thus far. In this paper, we examine several general-domain and domain-specific pre-trained Roberta variants and discover that general-domain fine-tuning does not really help generalization which aligns with the discovery of prior art, yet proper domain-specific fine-tuning with clickstream data can lead to better model generalization, based on a bucketed analysis of a manually annotated query-product relevance data.

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DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models
Hongyu Zhu | Yan Chen | Jing Yan | Jing Liu | Yu Hong | Ying Chen | Hua Wu | Haifeng Wang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

In this paper, we focus on the robustness evaluation of Chinese Question Matching (QM) models. Most of the previous work on analyzing robustness issues focus on just one or a few types of artificial adversarial examples. Instead, we argue that a comprehensive evaluation should be conducted on natural texts, which takes into account the fine-grained linguistic capabilities of QM models. For this purpose, we create a Chinese dataset namely DuQM which contains natural questions with linguistic perturbations to evaluate the robustness of QM models. DuQM contains 3 categories and 13 subcategories with 32 linguistic perturbations. The extensive experiments demonstrate that DuQM has a better ability to distinguish different models. Importantly, the detailed breakdown of evaluation by the linguistic phenomena in DuQM helps us easily diagnose the strength and weakness of different models. Additionally, our experiment results show that the effect of artificial adversarial examples does not work on natural texts. Our baseline codes and a leaderboard are now publicly available.

2021

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Gender Bias in Natural Language Processing Across Human Languages
Abigail Matthews | Isabella Grasso | Christopher Mahoney | Yan Chen | Esma Wali | Thomas Middleton | Mariama Njie | Jeanna Matthews
Proceedings of the First Workshop on Trustworthy Natural Language Processing

Natural Language Processing (NLP) systems are at the heart of many critical automated decision-making systems making crucial recommendations about our future world. Gender bias in NLP has been well studied in English, but has been less studied in other languages. In this paper, a team including speakers of 9 languages - Chinese, Spanish, English, Arabic, German, French, Farsi, Urdu, and Wolof - reports and analyzes measurements of gender bias in the Wikipedia corpora for these 9 languages. We develop extensions to profession-level and corpus-level gender bias metric calculations originally designed for English and apply them to 8 other languages, including languages that have grammatically gendered nouns including different feminine, masculine, and neuter profession words. We discuss future work that would benefit immensely from a computational linguistics perspective.

2017

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AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine
Minghui Qiu | Feng-Lin Li | Siyu Wang | Xing Gao | Yan Chen | Weipeng Zhao | Haiqing Chen | Jun Huang | Wei Chu
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We propose AliMe Chat, an open-domain chatbot engine that integrates the joint results of Information Retrieval (IR) and Sequence to Sequence (Seq2Seq) based generation models. AliMe Chat uses an attentive Seq2Seq based rerank model to optimize the joint results. Extensive experiments show our engine outperforms both IR and generation based models. We launch AliMe Chat for a real-world industrial application and observe better results than another public chatbot.

2014

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Exploiting Timelines to Enhance Multi-document Summarization
Jun-Ping Ng | Yan Chen | Min-Yen Kan | Zhoujun Li
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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A Semi-Supervised Bayesian Network Model for Microblog Topic Classification
Yan Chen | Zhoujun Li | Liqiang Nie | Xia Hu | Xiangyu Wang | Tat-Seng Chua | Xiaoming Zhang
Proceedings of COLING 2012