Ming‐Wei Chang

Modeling Large-Scale Structured Relationships with Shared Memory for Knowledge Base Completion

2017

Recent studies on knowledge base completion, the task of recovering missing relationships based on recorded relations, demonstrate the importance of learning embeddings from multi-step relations. However, due to the size of knowledge bases, learning multi-step …

S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet Entity Linking

2016 · arXiv (Cornell University)

Non-linear models recently receive a lot of attention as people are starting to discover the power of statistical and embedding features. However, tree-based models are seldom studied in the context of structured learning despite their …

Promptagator: Few-shot Dense Retrieval From 8 Examples

2022 · arXiv (Cornell University)

Much recent research on information retrieval has focused on how to transfer from one task (typically with abundant supervised data) to various other tasks where supervision is limited, with the implicit assumption that it is …

ASQA: Factoid Questions Meet Long-Form Answers

2022

Recent progress on open domain factoid question answering (QA) does not easily transfer to the task of long-form QA, where the goal is to answer questions that require in-depth explanations. The hurdles include a lack …

Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base

2015

Wen-tau Yih, Ming-Wei Chang, Xiaodong He, Jianfeng Gao. 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.

S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet Entity Linking

2015

Yi Yang, Ming-Wei Chang. 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.

The Value of Semantic Parse Labeling for Knowledge Base Question Answering

2016

We demonstrate the value of collecting semantic parse labels for knowledge base question answering. In particular, (1) unlike previous studies on small-scale datasets, we show that learning from labeled semantic parses significantly improves overall performance, …

A Knowledge-Grounded Neural Conversation Model

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

Neural network models are capable of generating extremely natural sounding conversational interactions. However, these models have been mostly applied to casual scenarios (e.g., as “chatbots”) and have yet to demonstrate they can serve in more …

Search-based Neural Structured Learning for Sequential Question Answering

2017

Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore …

Natural Questions: A Benchmark for Question Answering Research

2019 · Transactions of the Association for Computational Linguistics

We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia …

BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

2019 · arXiv (Cornell University)

In this paper we study yes/no questions that are naturally occurring --- meaning that they are generated in unprompted and unconstrained settings. We build a reading comprehension dataset, BoolQ, of such questions, and show that …

REALM: Retrieval-Augmented Language Model Pre-Training

2020 · arXiv (Cornell University)

Language model pre-training has been shown to capture a surprising amount of world knowledge, crucial for NLP tasks such as question answering. However, this knowledge is stored implicitly in the parameters of a neural network, …

Large Dual Encoders Are Generalizable Retrievers

2022

Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, Yinfei Yang. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. …