Answering Complex Questions in the Wild

When:
Thursday, October 17, 2019, 11:00 am - 12:00 pm PDTiCal
Where:
6th floor conference room: #689
This event is open to the public.
Type:
NL Seminar
Speaker:
Peng Qi (Stanford Univ)
Video Recording:
https://bluejeans.com/265217611
Description:

Abstract: 

Open-domain question answering (open-domain QA) systems greatly improve our access to the knowledge in large text corpora, but most previous work on this topic lacks the ability to perform multi-hop reasoning, limiting how textual knowledge can actually be used. For instance, to answer "What's the Aquaman actor's next movie?", one needs to reason about the entity "Jason Momoa" instead of just comparing the question to a local context, making the task more challenging.

In this talk, I will present our recent work on enabling text-based multi-hop reasoning in open-domain question answering. First, I will talk about how we collected one of the first datasets on multi-hop QA, making it possible to train and evaluate systems to perform explainable complex reasoning among millions of Wikipedia articles. Then, I will present a QA system we developed on this dataset. Iterating between finding supporting facts and reading the retrieved context, our model outperforms all previously published approaches, many of which based on powerful pretrained neural networks like BERT. As our model generates natural language queries at each step of its retrieval, it is also readily explainable to humans, and allows for intervention when it veers off course. I will conclude by comparing our model to other recent developments on this dataset, and discussing future directions on this problem.

BIO:

Peng Qi is a PhD student in Computer Science at Stanford University. His research interests revolve around building natural language processing systems that better bridge between humans and the large amount of (textual) information we are engulfed in. Specifically, he is interested in building knowledge representations, (open-domain) question answering, explainable models, and multi-lingual NLP systems. He is also interested in linguistics, and builds tools for linguistic structure analysis applicable to many languages.

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