Ting Liu

Capturing the Semantics of Key Phrases Using Multiple Languages for Question Retrieval

2015 · IEEE Transactions on Knowledge and Data Engineering

In the age of Web 2.0, community user contributed questions and answers provide an important alternative for knowledge acquisition through web search. Question retrieval in current community-based question answering (CQA) services do not, in general, …

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 …

SCIR-QA at SemEval-2017 Task 3: CNN Model Based on Similar and Dissimilar Information between Keywords for Question Similarity

2017

We describe a method of calculating the similarity between questions in community QA. Questions in cQA are usually very long and there are a lot of useless information about calculating the similarity between questions. Therefore, …

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. …

Topic-to-Essay Generation with Neural Networks

2018

We focus on essay generation, which is a challenging task that generates a paragraph-level text with multiple topics.Progress towards understanding different topics and expressing diversity in this task requires more powerful generators and richer training …

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 …

e-CARE: a New Dataset for Exploring Explainable Causal Reasoning

2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Understanding causality has vital importance for various Natural Language Processing (NLP) applications. Beyond the labeled instances, conceptual explanations of the causality can provide deep understanding of the causal facts to facilitate the causal reasoning process. …

Joint Data and Model Driven Channel-Free Signal Detection based Learned Factor Graph

2022 · 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

We propose a learned factor graph based on convolutional neural network (CNN) and Bi-directional Long Short Term Memory (BiLSTM) to realize signal detection under the scenario of no channel model. It can solve the inevitable …

Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications

2023 · arXiv (Cornell University)

Large language models (LLMs) exhibit superior performance on various natural language tasks, but they are susceptible to issues stemming from outdated data and domain-specific limitations. In order to address these challenges, researchers have pursued two …

Aligning Translation-Specific Understanding to General Understanding in Large Language Models

2024 · arXiv (Cornell University)

Large Language models (LLMs) have exhibited remarkable abilities in understanding complex texts, offering a promising path towards human-like translation performance. However, this study reveals the misalignment between the translation-specific understanding and the general understanding inside …

Asymptotic Consistent Graph Structure Learning for Multivariate Time-Series Anomaly Detection

2024 · IEEE Transactions on Instrumentation and Measurement

Capturing complex inter-variable relationships is crucial for anomaly detection for multivariate time series (MTS) data. In recent years, graph neural networks (GNNs) have been introduced to explicitly model complex inter-variable relationships from global static or …

OV-AS: Zero-Shot/Few-Shot Open-Vocabulary Anomaly Segmentation Based on CLIP

2024

Existing anomaly detection methods mainly focus on unsupervised learning, resulting in low generalization and single type segmentation. Hence, we introduce open-vocabulary detection pattern into anomaly segmentation field to achieve multi-semantic segmentation and propose a zero-shot/few-shot …

Final: Combining First-Order Logic With Natural Logic for Question Answering

2025 · IEEE Transactions on Knowledge and Data Engineering

Many question-answering problems can be approached as textual entailment tasks, where the hypotheses are formed by the question and candidate answers, and the premises are derived from an external knowledge base. However, current neural methods …

Detecting State Manipulation Vulnerabilities in Smart Contracts Using LLM and Static Analysis

2025 · arXiv (Cornell University)

An increasing number of DeFi protocols are gaining popularity, facilitating transactions among multiple anonymous users. State Manipulation is one of the notorious attacks in DeFi smart contracts, with price variable being the most commonly exploited …

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 …

Attention-over-Attention Neural Networks for Reading Comprehension

2017

Cloze-style reading comprehension is a representative problem in mining relationship between document and query. In this paper, we present a simple but novel model called attention-over-attention reader for better solving cloze-style reading comprehension task. The …

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 …

Hierarchical Attention Flow for Multiple-Choice Reading Comprehension

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

In this paper, we focus on multiple-choice reading comprehension which aims to answer a question given a passage and multiple candidate options. We present the hierarchical attention flow to adequately leverage candidate options to model …

Improving Low Resource Named Entity Recognition using Cross-lingual Knowledge Transfer

2018

Neural networks have been widely used for high resource language (e.g. English) named entity recognition (NER) and have shown state-of-the-art results.However, for low resource languages, such as Dutch, Spanish, due to the limitation of resources …

Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation

2019

We present a neural approach called IRNet for complex and cross-domain Text-to-SQL. IR-Net aims to address two challenges: 1) the mismatch between intents expressed in natural language (NL) and the implementation details in SQL; 2) …

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 …

A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

State-of-the-art studies have demonstrated the superiority of joint modeling over pipeline implementation for medical named entity recognition and normalization due to the mutual benefits between the two processes. To exploit these benefits in a more …

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 …

CodeBERT: A Pre-Trained Model for Programming and Natural Languages

2020

Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, Ming Zhou. Findings of the Association for Computational Linguistics: EMNLP 2020. 2020.

Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization

2021

Xiachong Feng, Xiaocheng Feng, Libo Qin, Bing Qin, Ting Liu. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long …