Mo Yu

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 …

Cross-lingual Knowledge Graph Alignment via Graph Matching Neural Network

2019 · arXiv (Cornell University)

Previous cross-lingual knowledge graph (KG) alignment studies rely on entity embeddings derived only from monolingual KG structural information, which may fail at matching entities that have different facts in two KGs. In this paper, we …

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 …

Diverse Few-Shot Text Classification with Multiple Metrics

2018

Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, Bowen Zhou. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human …

A Game Theoretic Approach to Class-wise Selective Rationalization.

2019 · DSpace@MIT (Massachusetts Institute of Technology)

Selection of input features such as relevant pieces of text has become a common technique of highlighting how complex neural predictors operate. The selection can be optimized post-hoc for trained models or incorporated directly into …

Do Multi-hop Readers Dream of Reasoning Chains?

2019 · arXiv (Cornell University)

General Question Answering (QA) systems over texts require the multi-hop reasoning capability, i.e. the ability to reason with information collected from multiple passages to derive the answer. In this paper we conduct a systematic analysis …

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 …

R$^3$: Reinforced Reader-Ranker for Open-Domain Question Answering

2017 · arXiv (Cornell University)

In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simplified closed-domain settings such as the SQuAD (Rajpurkar et …

Improving Question Answering over Incomplete KBs with Knowledge-Aware Reader

2019

We propose a new end-to-end question answering model, which learns to aggregate answer evidence from an incomplete knowledge base (KB) and a set of retrieved text snippets. Under the assumptions that the structured KB is …

Sentence Embedding Alignment for Lifelong Relation Extraction

2019

Hong Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long …

Improving Natural Language Inference Using External Knowledge in the Science Questions Domain

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

Natural Language Inference (NLI) is fundamental to many Natural Language Processing (NLP) applications including semantic search and question answering. The NLI problem has gained significant attention due to the release of large scale, challenging datasets. …

Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control

2019

Mo Yu, Shiyu Chang, Yang Zhang, Tommi Jaakkola. 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.