45,461 research outputs found
Simple and Effective Multi-Paragraph Reading Comprehension
We consider the problem of adapting neural paragraph-level question answering
models to the case where entire documents are given as input. Our proposed
solution trains models to produce well calibrated confidence scores for their
results on individual paragraphs. We sample multiple paragraphs from the
documents during training, and use a shared-normalization training objective
that encourages the model to produce globally correct output. We combine this
method with a state-of-the-art pipeline for training models on document QA
data. Experiments demonstrate strong performance on several document QA
datasets. Overall, we are able to achieve a score of 71.3 F1 on the web portion
of TriviaQA, a large improvement from the 56.7 F1 of the previous best system.Comment: 11 pages, updated a referenc
Simple and Effective Multi-Paragraph Reading Comprehension
We consider the problem of adapting neural paragraph-level question answering
models to the case where entire documents are given as input. Our proposed
solution trains models to produce well calibrated confidence scores for their
results on individual paragraphs. We sample multiple paragraphs from the
documents during training, and use a shared-normalization training objective
that encourages the model to produce globally correct output. We combine this
method with a state-of-the-art pipeline for training models on document QA
data. Experiments demonstrate strong performance on several document QA
datasets. Overall, we are able to achieve a score of 71.3 F1 on the web portion
of TriviaQA, a large improvement from the 56.7 F1 of the previous best system.Comment: 11 pages, updated a referenc
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
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
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