411 research outputs found
Reading Wikipedia to Answer Open-Domain Questions
This paper proposes to tackle open- domain question answering using Wikipedia
as the unique knowledge source: the answer to any factoid question is a text
span in a Wikipedia article. This task of machine reading at scale combines the
challenges of document retrieval (finding the relevant articles) with that of
machine comprehension of text (identifying the answer spans from those
articles). Our approach combines a search component based on bigram hashing and
TF-IDF matching with a multi-layer recurrent neural network model trained to
detect answers in Wikipedia paragraphs. Our experiments on multiple existing QA
datasets indicate that (1) both modules are highly competitive with respect to
existing counterparts and (2) multitask learning using distant supervision on
their combination is an effective complete system on this challenging task.Comment: ACL2017, 10 page
How much should you ask? On the question structure in QA systems
Datasets that boosted state-of-the-art solutions for Question Answering (QA)
systems prove that it is possible to ask questions in natural language manner.
However, users are still used to query-like systems where they type in keywords
to search for answer. In this study we validate which parts of questions are
essential for obtaining valid answer. In order to conclude that, we take
advantage of LIME - a framework that explains prediction by local
approximation. We find that grammar and natural language is disregarded by QA.
State-of-the-art model can answer properly even if 'asked' only with a few
words with high coefficients calculated with LIME. According to our knowledge,
it is the first time that QA model is being explained by LIME.Comment: Accepted to Analyzing and interpreting neural networks for NLP
workshop at EMNLP 201
Does it care what you asked? Understanding Importance of Verbs in Deep Learning QA System
In this paper we present the results of an investigation of the importance of
verbs in a deep learning QA system trained on SQuAD dataset. We show that main
verbs in questions carry little influence on the decisions made by the system -
in over 90% of researched cases swapping verbs for their antonyms did not
change system decision. We track this phenomenon down to the insides of the
net, analyzing the mechanism of self-attention and values contained in hidden
layers of RNN. Finally, we recognize the characteristics of the SQuAD dataset
as the source of the problem. Our work refers to the recently popular topic of
adversarial examples in NLP, combined with investigating deep net structure.Comment: Accepted to Analyzing and interpreting neural networks for NLP
workshop at EMNLP 201
Fine-tuning Multi-hop Question Answering with Hierarchical Graph Network
In this paper, we present a two stage model for multi-hop question answering.
The first stage is a hierarchical graph network, which is used to reason over
multi-hop question and is capable to capture different levels of granularity
using the nature structure(i.e., paragraphs, questions, sentences and entities)
of documents. The reasoning process is convert to node classify task(i.e.,
paragraph nodes and sentences nodes). The second stage is a language model
fine-tuning task. In a word, stage one use graph neural network to select and
concatenate support sentences as one paragraph, and stage two find the answer
span in language model fine-tuning paradigm.Comment: the experience result is not as good as I excep
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