An Abstractive approach to Question Answering
By Rajarshee Mitra. 2017
5 citations recorded by the source corpus.
Question Answering has come a long way from answer sentence selection, relational QA to reading and comprehension. We move our attention to abstractive question answering by which we facilitate machine to read passages and answer questions by generating them. We frame the problem as a sequence to sequence learning where the encoder being a network that models the relation between question and passage, thereby relying solely on passage and question content to form an abstraction of the answer. Not being able to retain facts and making repetitions are common mistakes that affect the overall legibility of answers. To counter these issues, we employ copying mechanism and maintenance of coverage vector in our model respectively. Our results on MS-MARCO demonstrates it's superiority over baselines and we also show qualitative examples where we improved in terms of correctness and readability.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing
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
- Eunsol Choi
- Luke S. Zettlemoyer
- Tom Kwiatkowski
- Percy Liang
- Ming Yan
- Wei Wang
- Chenliang Li
- Hannaneh Hajishirzi
In Computer Vision and Pattern Recognition
- Chenliang Li
- Ming Yan
- Wei Wang
- Bin Bi
- Eunsol Choi
- Tom Kwiatkowski
- Michael Collins
- Jennimaria Palomaki
In Natural Language Processing
- Eunsol Choi
- Luke S. Zettlemoyer
- Tom Kwiatkowski
- Percy Liang
- Jonathan Berant
- Michael Collins
- Christopher D. Manning
- Greg Durrett
Citations in the corpus
- TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages (2020, background): “The question answering format encompasses a wide variety of tasks (Gardner et al., 2019) ranging from generating an answer word-by-word (Mitra, 2017) or finding an answer from within an entire corpus as in TREC (Voorhees and Tice, 2000) and DrQA (Chen et al., 2017).”
- Incorporating External Knowledge into Machine Reading for Generative Question Answering (2019, background): “Mitra (2017) proposed a seq2seq-based model that learns alignment between a question and passage words to produce rich question-aware passage representation by which it directly decodes an answer.”