Daya Guo

Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base

2019 · arXiv (Cornell University)

We consider the problem of conversational question answering over a large-scale knowledge base. To handle huge entity vocabulary of a large-scale knowledge base, recent neural semantic parsing based approaches usually decompose the task into several …

ReACC: A Retrieval-Augmented Code Completion Framework

2022 · arXiv (Cornell University)

Code completion, which aims to predict the following code token(s) according to the code context, can improve the productivity of software development. Recent work has proved that statistical language modeling with transformers can greatly improve …

Noisy Pair Corrector for Dense Retrieval

2023

Most dense retrieval models contain an implicit assumption: the training query-document pairs are exactly matched. Since it is expensive to annotate the corpus manually, training pairs in real-world applications are usually collected automatically, which inevitably …

Dialog-to-action: conversational question answering over a large-scale knowledge base

2018 · Neural Information Processing Systems

We present an approach to map utterances in conversation to logical forms, which will be executed on a large-scale knowledge base. To handle enormous ellipsis phenomena in conversation, we introduce dialog memory management to manipulate …

Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

Commonsense question answering aims to answer questions which require background knowledge that is not explicitly expressed in the question. The key challenge is how to obtain evidence from external knowledge and make predictions based on …

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.