5,614 research outputs found
Towards Understanding and Answering Multi-Sentence Recommendation Questions on Tourism
We introduce the first system towards the novel task of answering complex
multisentence recommendation questions in the tourism domain. Our solution uses
a pipeline of two modules: question understanding and answering. For question
understanding, we define an SQL-like query language that captures the semantic
intent of a question; it supports operators like subset, negation, preference
and similarity, which are often found in recommendation questions. We train and
compare traditional CRFs as well as bidirectional LSTM-based models for
converting a question to its semantic representation. We extend these models to
a semisupervised setting with partially labeled sequences gathered through
crowdsourcing. We find that our best model performs semi-supervised training of
BiDiLSTM+CRF with hand-designed features and CCM(Chang et al., 2007)
constraints. Finally, in an end to end QA system, our answering component
converts our question representation into queries fired on underlying knowledge
sources. Our experiments on two different answer corpora demonstrate that our
system can significantly outperform baselines with up to 20 pt higher accuracy
and 17 pt higher recall
Improved Answer Selection with Pre-Trained Word Embeddings
This paper evaluates existing and newly proposed answer selection methods
based on pre-trained word embeddings. Word embeddings are highly effective in
various natural language processing tasks and their integration into
traditional information retrieval (IR) systems allows for the capture of
semantic relatedness between questions and answers. Empirical results on three
publicly available data sets show significant gains over traditional term
frequency based approaches in both supervised and unsupervised settings. We
show that combining these word embedding features with traditional
learning-to-rank techniques can achieve similar performance to state-of-the-art
neural networks trained for the answer selection task
Dense Passage Retrieval for Open-Domain Question Answering
Open-domain question answering relies on efficient passage retrieval to
select candidate contexts, where traditional sparse vector space models, such
as TF-IDF or BM25, are the de facto method. In this work, we show that
retrieval can be practically implemented using dense representations alone,
where embeddings are learned from a small number of questions and passages by a
simple dual-encoder framework. When evaluated on a wide range of open-domain QA
datasets, our dense retriever outperforms a strong Lucene-BM25 system largely
by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our
end-to-end QA system establish new state-of-the-art on multiple open-domain QA
benchmarks.Comment: EMNLP 202
Unifying Question Answering, Text Classification, and Regression via Span Extraction
Even as pre-trained language encoders such as BERT are shared across many
tasks, the output layers of question answering, text classification, and
regression models are significantly different. Span decoders are frequently
used for question answering, fixed-class, classification layers for text
classification, and similarity-scoring layers for regression tasks, We show
that this distinction is not necessary and that all three can be unified as
span extraction. A unified, span-extraction approach leads to superior or
comparable performance in supplementary supervised pre-trained, low-data, and
multi-task learning experiments on several question answering, text
classification, and regression benchmarks.Comment: updating paper to also include regression task
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
In this paper we study yes/no questions that are naturally occurring ---
meaning that they are generated in unprompted and unconstrained settings. We
build a reading comprehension dataset, BoolQ, of such questions, and show that
they are unexpectedly challenging. They often query for complex, non-factoid
information, and require difficult entailment-like inference to solve. We also
explore the effectiveness of a range of transfer learning baselines. We find
that transferring from entailment data is more effective than transferring from
paraphrase or extractive QA data, and that it, surprisingly, continues to be
very beneficial even when starting from massive pre-trained language models
such as BERT. Our best method trains BERT on MultiNLI and then re-trains it on
our train set. It achieves 80.4% accuracy compared to 90% accuracy of human
annotators (and 62% majority-baseline), leaving a significant gap for future
work.Comment: In NAACL 201
Learned in Translation: Contextualized Word Vectors
Computer vision has benefited from initializing multiple deep layers with
weights pretrained on large supervised training sets like ImageNet. Natural
language processing (NLP) typically sees initialization of only the lowest
layer of deep models with pretrained word vectors. In this paper, we use a deep
LSTM encoder from an attentional sequence-to-sequence model trained for machine
translation (MT) to contextualize word vectors. We show that adding these
context vectors (CoVe) improves performance over using only unsupervised word
and character vectors on a wide variety of common NLP tasks: sentiment analysis
(SST, IMDb), question classification (TREC), entailment (SNLI), and question
answering (SQuAD). For fine-grained sentiment analysis and entailment, CoVe
improves performance of our baseline models to the state of the art
A Call for More Rigor in Unsupervised Cross-lingual Learning
We review motivations, definition, approaches, and methodology for
unsupervised cross-lingual learning and call for a more rigorous position in
each of them. An existing rationale for such research is based on the lack of
parallel data for many of the world's languages. However, we argue that a
scenario without any parallel data and abundant monolingual data is unrealistic
in practice. We also discuss different training signals that have been used in
previous work, which depart from the pure unsupervised setting. We then
describe common methodological issues in tuning and evaluation of unsupervised
cross-lingual models and present best practices. Finally, we provide a unified
outlook for different types of research in this area (i.e., cross-lingual word
embeddings, deep multilingual pretraining, and unsupervised machine
translation) and argue for comparable evaluation of these models.Comment: ACL 202
Cross-language Learning with Adversarial Neural Networks: Application to Community Question Answering
We address the problem of cross-language adaptation for question-question
similarity reranking in community question answering, with the objective to
port a system trained on one input language to another input language given
labeled training data for the first language and only unlabeled data for the
second language. In particular, we propose to use adversarial training of
neural networks to learn high-level features that are discriminative for the
main learning task, and at the same time are invariant across the input
languages. The evaluation results show sizable improvements for our
cross-language adversarial neural network (CLANN) model over a strong
non-adversarial system.Comment: CoNLL-2017: The SIGNLL Conference on Computational Natural Language
Learning; cross-language adversarial neural network (CLANN) model;
adversarial training; cross-language adaptation; community question
answering; question-question similarit
Why we have switched from building full-fledged taxonomies to simply detecting hypernymy relations
The study of taxonomies and hypernymy relations has been extensive on the
Natural Language Processing (NLP) literature. However, the evaluation of
taxonomy learning approaches has been traditionally troublesome, as it mainly
relies on ad-hoc experiments which are hardly reproducible and manually
expensive. Partly because of this, current research has been lately focusing on
the hypernymy detection task. In this paper we reflect on this trend, analyzing
issues related to current evaluation procedures. Finally, we propose three
potential avenues for future work so that is-a relations and resources based on
them play a more important role in downstream NLP applications.Comment: Discussion paper. 6 pages, 1 figur
Machine Learning with World Knowledge: The Position and Survey
Machine learning has become pervasive in multiple domains, impacting a wide
variety of applications, such as knowledge discovery and data mining, natural
language processing, information retrieval, computer vision, social and health
informatics, ubiquitous computing, etc. Two essential problems of machine
learning are how to generate features and how to acquire labels for machines to
learn. Particularly, labeling large amount of data for each domain-specific
problem can be very time consuming and costly. It has become a key obstacle in
making learning protocols realistic in applications. In this paper, we will
discuss how to use the existing general-purpose world knowledge to enhance
machine learning processes, by enriching the features or reducing the labeling
work. We start from the comparison of world knowledge with domain-specific
knowledge, and then introduce three key problems in using world knowledge in
learning processes, i.e., explicit and implicit feature representation,
inference for knowledge linking and disambiguation, and learning with direct or
indirect supervision. Finally we discuss the future directions of this research
topic
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