32,151 research outputs found
Open-Retrieval Conversational Question Answering
Conversational search is one of the ultimate goals of information retrieval.
Recent research approaches conversational search by simplified settings of
response ranking and conversational question answering, where an answer is
either selected from a given candidate set or extracted from a given passage.
These simplifications neglect the fundamental role of retrieval in
conversational search. To address this limitation, we introduce an
open-retrieval conversational question answering (ORConvQA) setting, where we
learn to retrieve evidence from a large collection before extracting answers,
as a further step towards building functional conversational search systems. We
create a dataset, OR-QuAC, to facilitate research on ORConvQA. We build an
end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader
that are all based on Transformers. Our extensive experiments on OR-QuAC
demonstrate that a learnable retriever is crucial for ORConvQA. We further show
that our system can make a substantial improvement when we enable history
modeling in all system components. Moreover, we show that the reranker
component contributes to the model performance by providing a regularization
effect. Finally, further in-depth analyses are performed to provide new
insights into ORConvQA.Comment: Accepted to SIGIR'2
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
Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives
This paper tackles the problem of reading comprehension over long narratives
where documents easily span over thousands of tokens. We propose a curriculum
learning (CL) based Pointer-Generator framework for reading/sampling over large
documents, enabling diverse training of the neural model based on the notion of
alternating contextual difficulty. This can be interpreted as a form of domain
randomization and/or generative pretraining during training. To this end, the
usage of the Pointer-Generator softens the requirement of having the answer
within the context, enabling us to construct diverse training samples for
learning. Additionally, we propose a new Introspective Alignment Layer (IAL),
which reasons over decomposed alignments using block-based self-attention. We
evaluate our proposed method on the NarrativeQA reading comprehension
benchmark, achieving state-of-the-art performance, improving existing baselines
by relative improvement on BLEU-4 and relative improvement on
Rouge-L. Extensive ablations confirm the effectiveness of our proposed IAL and
CL components.Comment: Accepted to ACL 201
Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension
This study considers the task of machine reading at scale (MRS) wherein,
given a question, a system first performs the information retrieval (IR) task
of finding relevant passages in a knowledge source and then carries out the
reading comprehension (RC) task of extracting an answer span from the passages.
Previous MRS studies, in which the IR component was trained without considering
answer spans, struggled to accurately find a small number of relevant passages
from a large set of passages. In this paper, we propose a simple and effective
approach that incorporates the IR and RC tasks by using supervised multi-task
learning in order that the IR component can be trained by considering answer
spans. Experimental results on the standard benchmark, answering SQuAD
questions using the full Wikipedia as the knowledge source, showed that our
model achieved state-of-the-art performance. Moreover, we thoroughly evaluated
the individual contributions of our model components with our new Japanese
dataset and SQuAD. The results showed significant improvements in the IR task
and provided a new perspective on IR for RC: it is effective to teach which
part of the passage answers the question rather than to give only a relevance
score to the whole passage.Comment: 10 pages, 6 figure. Accepted as a full paper at CIKM 201
Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering
Evidence retrieval is a critical stage of question answering (QA), necessary
not only to improve performance, but also to explain the decisions of the
corresponding QA method. We introduce a simple, fast, and unsupervised
iterative evidence retrieval method, which relies on three ideas: (a) an
unsupervised alignment approach to soft-align questions and answers with
justification sentences using only GloVe embeddings, (b) an iterative process
that reformulates queries focusing on terms that are not covered by existing
justifications, which (c) a stopping criterion that terminates retrieval when
the terms in the given question and candidate answers are covered by the
retrieved justifications. Despite its simplicity, our approach outperforms all
the previous methods (including supervised methods) on the evidence selection
task on two datasets: MultiRC and QASC. When these evidence sentences are fed
into a RoBERTa answer classification component, we achieve state-of-the-art QA
performance on these two datasets.Comment: Accepted at ACL 2020 as a long conference pape
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