Wenpeng Yin

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 …

End-Task Oriented Textual Entailment via Deep Explorations of Inter-Sentence Interactions

2018 · arXiv (Cornell University)

This work deals with SciTail, a natural entailment challenge derived from a multi-choice question answering problem. The premises and hypotheses in SciTail were generated with no awareness of each other, and did not specifically aim …

TwoWingOS: A Two-Wing Optimization Strategy for Evidential Claim Verification

2018 · arXiv (Cornell University)

Determining whether a given claim is supported by evidence is a fundamental NLP problem that is best modeled as Textual Entailment. However, given a large collection of text, finding evidence that could support or refute …

Large Language Model Instruction Following: A Survey of Progresses and Challenges

2023 · arXiv (Cornell University)

Task semantics can be expressed by a set of input-output examples or a piece of textual instruction. Conventional machine learning approaches for natural language processing (NLP) mainly rely on the availability of large-scale sets of …

A Generic Method for Fine-grained Category Discovery in Natural Language Texts

2024 · arXiv (Cornell University)

Fine-grained category discovery using only coarse-grained supervision is a cost-effective yet challenging task. Previous training methods focus on aligning query samples with positive samples and distancing them from negatives. They often neglect intra-category and inter-category …

Large Language Models for Mathematical Reasoning: Progresses and Challenges

2024

Janice Ahn, Rishu Verma, Renze Lou, Di Liu, Rui Zhang, Wenpeng Yin. Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop. 2024.

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 …

Multichannel Variable-Size Convolution for Sentence Classification

2015

We propose MVCNN, a convolution neural network (CNN) architecture for sentence classification. It (i) combines diverse versions of pretrained word embeddings and (ii) extracts features of multigranular phrases with variable-size convolution filters. We also show …

Convolutional Neural Network for Paraphrase Identification

2015

We present a new deep learning architecture Bi-CNN-MI for paraphrase identification (PI). Based on the insight that PI requires comparing two sentences on multiple levels of granularity, we learn multigranular sentence representations using convolutional neural …

Learning Word Meta-Embeddings

2016

Word embeddings -distributed representations of words -in deep learning are beneficial for many tasks in NLP. However, different embedding sets vary greatly in quality and characteristics of the captured information. Instead of relying on a …

Comparative Study of CNN and RNN for Natural Language Processing

2017 · arXiv (Cornell University)

Deep neural networks (DNN) have revolutionized the field of natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN), the two main types of DNN architectures, are widely explored to handle various …

Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach

2019

Wenpeng Yin, Jamaal Hay, Dan Roth. 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.