scholarly journals CoQA: A Conversational Question Answering Challenge

2019 ◽  
Vol 7 ◽  
pp. 249-266 ◽  
Author(s):  
Siva Reddy ◽  
Danqi Chen ◽  
Christopher D. Manning
Keyword(s):  
Reading Comprehension ◽  
Human Performance ◽  
Question Answering ◽  
Information Gathering ◽  
Free Form ◽  
Question Answering Systems ◽  
Questions And Answers ◽  
Pragmatic Reasoning

Humans gather information through conversations involving a series of interconnected questions and answers. For machines to assist in information gathering, it is therefore essential to enable them to answer conversational questions. We introduce CoQA, a novel dataset for building Conversational Question Answering systems. Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. We analyze CoQA in depth and show that conversational questions have challenging phenomena not present in existing reading comprehension datasets (e.g., coreference and pragmatic reasoning). We evaluate strong dialogue and reading comprehension models on CoQA. The best system obtains an F1 score of 65.4%, which is 23.4 points behind human performance (88.8%), indicating that there is ample room for improvement. We present CoQA as a challenge to the community at https://stanfordnlp.github.io/coqa .

scholarly journals Structured Two-Stream Attention Network for Video Question Answering

2019 ◽  
Vol 33 ◽  
pp. 6391-6398 ◽  
Author(s):  
Lianli Gao ◽  
Pengpeng Zeng ◽  
Jingkuan Song ◽  
Yuan-Fang Li ◽  
Wu Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Question Answering ◽  
Free Form ◽  
Image Region ◽  
Attention Network ◽  
Natural Language Question ◽  
Temporal Structures ◽  
Text Features ◽  
Video Input ◽  
Language Question

To date, visual question answering (VQA) (i.e., image QA and video QA) is still a holy grail in vision and language understanding, especially for video QA. Compared with image QA that focuses primarily on understanding the associations between image region-level details and corresponding questions, video QA requires a model to jointly reason across both spatial and long-range temporal structures of a video as well as text to provide an accurate answer. In this paper, we specifically tackle the problem of video QA by proposing a Structured Two-stream Attention network, namely STA, to answer a free-form or open-ended natural language question about the content of a given video. First, we infer rich longrange temporal structures in videos using our structured segment component and encode text features. Then, our structured two-stream attention component simultaneously localizes important visual instance, reduces the influence of background video and focuses on the relevant text. Finally, the structured two-stream fusion component incorporates different segments of query and video aware context representation and infers the answers. Experiments on the large-scale video QA dataset TGIF-QA show that our proposed method significantly surpasses the best counterpart (i.e., with one representation for the video input) by 13.0%, 13.5%, 11.0% and 0.3 for Action, Trans., TrameQA and Count tasks. It also outperforms the best competitor (i.e., with two representations) on the Action, Trans., TrameQA tasks by 4.1%, 4.7%, and 5.1%.

scholarly journals A framework for enriching Data Warehouse analysis with Question Answering systems

2014 ◽  
Vol 46 (1) ◽  
pp. 61-82 ◽  
Author(s):  
Antonio Ferrández ◽  
Alejandro Maté ◽  
Jesús Peral ◽  
Juan Trujillo ◽  
Elisa De Gregorio ◽  
...  
Keyword(s):  
Data Warehouse ◽  
Question Answering ◽  
Question Answering Systems

scholarly journals Composing Questions through Conceptual Authoring

2007 ◽  
Vol 33 (1) ◽  
pp. 105-133 ◽  
Author(s):  
Catalina Hallett ◽  
Donia Scott ◽  
Richard Power
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Free Text ◽  
Risk Averse ◽  
Proof Of Concept ◽  
Concept System ◽  
Complex Queries ◽  
Extensive Training ◽  
Question Answering Systems ◽  
Medical Histories

This article describes a method for composing fluent and complex natural language questions, while avoiding the standard pitfalls of free text queries. The method, based on Conceptual Authoring, is targeted at question-answering systems where reliability and transparency are critical, and where users cannot be expected to undergo extensive training in question composition. This scenario is found in most corporate domains, especially in applications that are risk-averse. We present a proof-of-concept system we have developed: a question-answering interface to a large repository of medical histories in the area of cancer. We show that the method allows users to successfully and reliably compose complex queries with minimal training.

scholarly journals Translucent Answer Predictions in Multi-Hop Reading Comprehension

2020 ◽  
Vol 34 (05) ◽  
pp. 7700-7707
Author(s):  
G P Shrivatsa Bhargav ◽  
Michael Glass ◽  
Dinesh Garg ◽  
Shirish Shevade ◽  
Saswati Dana ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Question Answering ◽  
State Of The Art ◽  
Local Context ◽  
The Novel ◽  
Loose Coupling ◽  
Loosely Coupled ◽  
Neural Architecture ◽  
Coupled Networks ◽  
Novel Design

Research on the task of Reading Comprehension style Question Answering (RCQA) has gained momentum in recent years due to the emergence of human annotated datasets and associated leaderboards, for example CoQA, HotpotQA, SQuAD, TriviaQA, etc. While state-of-the-art has advanced considerably, there is still ample opportunity to advance it further on some important variants of the RCQA task. In this paper, we propose a novel deep neural architecture, called TAP (Translucent Answer Prediction), to identify answers and evidence (in the form of supporting facts) in an RCQA task requiring multi-hop reasoning. TAP comprises two loosely coupled networks – Local and Global Interaction eXtractor (LoGIX) and Answer Predictor (AP). LoGIX predicts supporting facts, whereas AP consumes these predicted supporting facts to predict the answer span. The novel design of LoGIX is inspired by two key design desiderata – local context and global interaction– that we identified by analyzing examples of multi-hop RCQA task. The loose coupling between LoGIX and the AP reveals the set of sentences used by the AP in predicting an answer. Therefore, answer predictions of TAP can be interpreted in a translucent manner. TAP offers state-of-the-art performance on the HotpotQA (Yang et al. 2018) dataset – an apt dataset for multi-hop RCQA task – as it occupies Rank-1 on its leaderboard (https://hotpotqa.github.io/) at the time of submission.

Sign in / Sign up

Export Citation Format

Share Document