Bing Xiang

Improved Neural Relation Detection for Knowledge Base Question Answering

2017 · arXiv (Cornell University)

Relation detection is a core component for many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning that detects KB relations given …

A Structured Self-Attentive Sentence Embedding.

2017 · International Conference on Learning Representations

This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention. Instead of using a vector, we use a 2-D matrix to represent the embedding, with each row of the matrix …

Embedding-based Zero-shot Retrieval through Query Generation

2020 · arXiv (Cornell University)

Passage retrieval addresses the problem of locating relevant passages, usually from a large corpus, given a query. In practice, lexical term-matching algorithms like BM25 are popular choices for retrieval owing to their efficiency. However, term-based …

Retrieval, Re-ranking and Multi-task Learning for Knowledge-Base Question Answering

2021

Question answering over knowledge bases (KBQA) usually involves three sub-tasks, namely topic entity detection, entity linking and relation detection. Due to the large number of entities and relations inside knowledge bases (KB), previous work usually …

Entity-level Factual Consistency of Abstractive Text Summarization

2021

Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cicero Nogueira dos Santos, Henghui Zhu, Dejiao Zhang, Kathleen McKeown, Bing Xiang. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. …

REKnow: Enhanced Knowledge for Joint Entity and Relation Extraction

2022 · arXiv (Cornell University)

Relation extraction is an important but challenging task that aims to extract all hidden relational facts from the text. With the development of deep language models, relation extraction methods have achieved good performance on various …

Learning Dialogue Representations from Consecutive Utterances

2022 · arXiv (Cornell University)

Learning high-quality dialogue representations is essential for solving a variety of dialogue-oriented tasks, especially considering that dialogue systems often suffer from data scarcity. In this paper, we introduce Dialogue Sentence Embedding (DSE), a self-supervised contrastive …

Efficient Shapley Values Estimation by Amortization for Text Classification

2023 · arXiv (Cornell University)

Despite the popularity of Shapley Values in explaining neural text classification models, computing them is prohibitive for large pretrained models due to a large number of model evaluations. In practice, Shapley Values are often estimated …

Classifying Relations by Ranking with Convolutional Neural Networks

2015 · arXiv (Cornell University)

Cícero dos Santos, Bing Xiang, Bowen Zhou. 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 Papers). 2015.

LSTM-based Deep Learning Models for Non-factoid Answer Selection

2015 · arXiv (Cornell University)

In this paper, we apply a general deep learning (DL) framework for the answer selection task, which does not depend on manually defined features or linguistic tools. The basic framework is to build the embeddings …

ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs

2016 · Transactions of the Association for Computational Linguistics

How to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work (i) deals with one individual …

Attentive Pooling Networks

2016 · arXiv (Cornell University)

In this work, we propose Attentive Pooling (AP), a two-way attention mechanism for discriminative model training. In the context of pair-wise ranking or classification with neural networks, AP enables the pooling layer to be aware …

Improved Representation Learning for Question Answer Matching

2016

Passage-level question answer matching is a challenging task since it requires effective representations that capture the complex semantic relations between questions and answers. In this work, we propose a series of deep learning models to …

Applying deep learning to answer selection: A study and an open task

2015

We apply a general deep learning framework to address the non-factoid question answering task. Our approach does not rely on any linguistic tools and can be applied to different languages or domains. Various architectures are …

Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering

2019

Zhiguo Wang, Patrick Ng, Xiaofei Ma, Ramesh Nallapati, Bing Xiang. 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.

Universal Text Representation from BERT: An Empirical Study

2019 · arXiv (Cornell University)

We present a systematic investigation of layer-wise BERT activations for general-purpose text representations to understand what linguistic information they capture and how transferable they are across different tasks. Sentence-level embeddings are evaluated against two state-of-the-art …

Supporting Clustering with Contrastive Learning

2021

Dejiao Zhang, Feng Nan, Xiaokai Wei, Shang-Wen Li, Henghui Zhu, Kathleen McKeown, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: …