Wanxiang Che

Exploring Segment Representations for Neural Segmentation Models

2016 · arXiv (Cornell University)

Many natural language processing (NLP) tasks can be generalized into segmentation problem. In this paper, we combine semi-CRF with neural network to solve NLP segmentation tasks. Our model represents a segment both by composing the …

Distilling Knowledge for Search-based Structured Prediction

2018 · arXiv (Cornell University)

Many natural language processing tasks can be modeled into structured prediction and solved as a search problem. In this paper, we distill an ensemble of multiple models trained with different initialization into a single model. …

A Corpus-free State2Seq User Simulator for Task-oriented Dialogue

2019 · arXiv (Cornell University)

Recent reinforcement learning algorithms for task-oriented dialogue system absorbs a lot of interest. However, an unavoidable obstacle for training such algorithms is that annotated dialogue corpora are often unavailable. One of the popular approaches addressing …

Discriminative Sentence Modeling for Story Ending Prediction

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

Story Ending Prediction is a task that needs to select an appropriate ending for the given story, which requires the machine to understand the story and sometimes needs commonsense knowledge. To tackle this task, we …

Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification

2021 · Proceedings of the AAAI Conference on Artificial Intelligence

In a dialog system, dialog act recognition and sentiment classification are two correlative tasks to capture speakers’ intentions, where dialog act and sentiment can indicate the explicit and the implicit intentions separately. The dialog context …

NL-Augmenter 🦎 → 🐍 A Framework for Task-Sensitive Natural Language Augmentation

2023 · Northern European Journal of Language Technology

Data augmentation is an important method for evaluating the robustness of and enhancing the diversity of training data for natural language processing (NLP) models. In this paper, we present NL-Augmenter, a new participatory Python-based natural …

Overview of CTC 2021: Chinese Text Correction for Native Speakers

2022 · arXiv (Cornell University)

In this paper, we present an overview of the CTC 2021, a Chinese text correction task for native speakers. We give detailed descriptions of the task definition and the data for training as well as …

MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System

2023

Multi-modal sarcasm detection has attracted much recent attention. Nevertheless, the existing benchmark (MMSD) has some shortcomings that hinder the development of reliable multi-modal sarcasm detection system: (1) There are some spurious cues in MMSD, leading …

Large Language Models Meet NLP: A Survey

2024 · arXiv (Cornell University)

While large language models (LLMs) like ChatGPT have shown impressive capabilities in Natural Language Processing (NLP) tasks, a systematic investigation of their potential in this field remains largely unexplored. This study aims to address this …

Concise and Precise Context Compression for Tool-Using Language Models

2024

Yang Xu, Yunlong Feng, Honglin Mu, Yutai Hou, Yitong Li, Xinghao Wang, Wanjun Zhong, Zhongyang Li, Dandan Tu, Qingfu Zhu, Min Zhang, Wanxiang Che. Findings of the Association for Computational Linguistics: ACL 2024. 2024.

RE$^2$: Improving Chinese Grammatical Error Correction via Retrieving Appropriate Examples with Explanation

2025 · arXiv (Cornell University)

The primary objective of Chinese grammatical error correction (CGEC) is to detect and correct errors in Chinese sentences. Recent research shows that large language models (LLMs) have been applied to CGEC with significant results. For …

AutoPR: Let's Automate Your Academic Promotion!

2025 · arXiv (Cornell University)

As the volume of peer-reviewed research surges, scholars increasingly rely on social platforms for discovery, while authors invest considerable effort in promoting their work to ensure visibility and citations. To streamline this process and reduce …

Cross-lingual Dependency Parsing Based on Distributed Representations

2015

Jiang Guo, Wanxiang Che, David Yarowsky, Haifeng Wang, Ting Liu. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long …

A Representation Learning Framework for Multi-Source Transfer Parsing

2016 · Proceedings of the AAAI Conference on Artificial Intelligence

Cross-lingual model transfer has been a promising approach for inducing dependency parsers for low-resource languages where annotated treebanks are not available. The major obstacles for the model transfer approach are two-fold: 1. Lexical features are …

Towards Better

2018 · Proceedings of the

This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. We base our submission on Stanford's winning system for the CoNLL 2017 shared …

Cross-Lingual BERT Transformation for Zero-Shot Dependency Parsing

2019

Yuxuan Wang, Wanxiang Che, Jiang Guo, Yijia Liu, Ting Liu. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding

2019

Libo Qin, Wanxiang Che, Yangming Li, Haoyang Wen, Ting Liu. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Revisiting Pre-Trained Models for Chinese Natural Language Processing

2020

Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pretrained language models. In this paper, we target …

Data augmentation approaches in natural language processing: A survey

2022 · AI Open

As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where deep learning techniques may fail. It is widely applied in computer vision then introduced to natural language processing and achieves improvements in many …