Answering Complicated Question Intents Expressed in Decomposed Question Sequences
By Mohit Iyyer, Wen-tau Yih, Ming-Wei Chang. 2016
12 citations recorded by the source corpus.
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 a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We collect a dataset of 6,066 question sequences that inquire about semi-structured tables from Wikipedia, with 17,553 question-answer pairs in total. Existing QA systems face two major problems when evaluated on our dataset: (1) handling questions that contain coreferences to previous questions or answers, and (2) matching words or phrases in a question to corresponding entries in the associated table. We conclude by proposing strategies to handle both of these issues.
Subfields: Artificial Intelligence, Question Answering & Reading Comprehension
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Alon Talmor
- Jonathan Berant
- Percy Liang
- Ashish Sabharwal
- Hannaneh Hajishirzi
- Luke S. Zettlemoyer
- Mohit Bansal
- Peter Clark
In Question Answering & Reading Comprehension
- Jonathan Berant
- Alon Talmor
- Ashish Sabharwal
- Tushar Khot
- Peter Clark
- Hannaneh Hajishirzi
- Yejin Choi
- Kyle Richardson
Citations in the corpus
- The Web as a Knowledge-Base for Answering Complex Questions (2018, background): “Another important related research direction is Iyyer et al. (2016), who answered complex questions by decomposing them.”