85 citations · 252 across the 18 of their papers we have counts for
18 papers
Constituency Parsing using LLMs
Xuefeng Bai, Jialong Wu, Yulong Chen +4
Constituency parsing is a fundamental yet unsolved challenge in natural language processing. In this paper, we examine the potential of recent large language models (LLMs) to addre…
Explicit Syntactic Guidance for Neural Text Generation
Yafu Li, Leyang Cui, Jianhao Yan +4
Most existing text generation models follow the sequence-to-sequence paradigm. Generative Grammar suggests that humans generate natural language texts by learning language grammar.…
Opinion Tree Parsing for Aspect-based Sentiment Analysis
Xiaoyi Bao, Xiaotong Jiang, Zhongqing Wang +2
Extracting sentiment elements using pre-trained generative models has recently led to large improvements in aspect-based sentiment analysis benchmarks. However, these models always…
On the Role of Pre-trained Language Models in Word Ordering: A Case Study with BART
Zebin Ou, Meishan Zhang, Yue Zhang
Word ordering is a constrained language generation task taking unordered words as input. Existing work uses linear models and neural networks for the task, yet pre-trained language…
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing
Tao Gui, Xiao Wang, Qi Zhang +31
Various robustness evaluation methodologies from different perspectives have been proposed for different natural language processing (NLP) tasks. These methods have often focused o…
LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning
Jian Liu, Leyang Cui, Hanmeng Liu +3
Machine reading is a fundamental task for testing the capability of natural language understanding, which is closely related to human cognition in many aspects. With the rising of…
Subword Encoding in Lattice LSTM for Chinese Word Segmentation
Jie Yang, Yue Zhang, Shuailong Liang
We investigate a lattice LSTM network for Chinese word segmentation (CWS) to utilize words or subwords. It integrates the character sequence features with all subsequences informat…
Neural Transition-based Syntactic Linearization
Linfeng Song, Yue Zhang, Daniel Gildea
The task of linearization is to find a grammatical order given a set of words. Traditional models use statistical methods. Syntactic linearization systems, which generate a sentenc…
Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks
Linfeng Song, Zhiguo Wang, Mo Yu +3
Multi-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question. Prev…
Two Local Models for Neural Constituent Parsing
Zhiyang Teng, Yue Zhang
Non-local features have been exploited by syntactic parsers for capturing dependencies between sub output structures. Such features have been a key to the success of state-of-the-a…
A Graph-to-Sequence Model for AMR-to-Text Generation
Linfeng Song, Yue Zhang, Zhiguo Wang +1
The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph. The current state-of-the-art method uses a sequence-to-sequence mode…
Combining Discrete and Neural Features for Sequence Labeling
Jie Yang, Zhiyang Teng, Meishan Zhang +1
Neural network models have recently received heated research attention in the natural language processing community. Compared with traditional models with discrete features, neural…
Universal Dependencies Parsing for Colloquial Singaporean English
Hongmin Wang, Yue Zhang, GuangYong Leonard Chan +2
Singlish can be interesting to the ACL community both linguistically as a major creole based on English, and computationally for information extraction and sentiment analysis of re…
Neural Word Segmentation with Rich Pretraining
Jie Yang, Yue Zhang, Fei Dong
Neural word segmentation research has benefited from large-scale raw texts by leveraging them for pretraining character and word embeddings. On the other hand, statistical segmenta…
Shift-Reduce Constituent Parsing with Neural Lookahead Features
Jiangming Liu, Yue Zhang
Transition-based models can be fast and accurate for constituent parsing. Compared with chart-based models, they leverage richer features by extracting history information from a p…
Bidirectional Tree-Structured LSTM with Head Lexicalization
Zhiyang Teng, Yue Zhang
Sequential LSTM has been extended to model tree structures, giving competitive results for a number of tasks. Existing methods model constituent trees by bottom-up combinations of…
Training Dependency Parsers with Partial Annotation
Zhenghua Li, Yue Zhang, Jiayuan Chao +1
Recently, these has been a surge on studying how to obtain partially annotated data for model supervision. However, there still lacks a systematic study on how to train statistical…
AMR-to-text generation as a Traveling Salesman Problem
Linfeng Song, Yue Zhang, Xiaochang Peng +2
The task of AMR-to-text generation is to generate grammatical text that sustains the semantic meaning for a given AMR graph. We at- tack the task by first partitioning the AMR grap…