31,480 research outputs found
Putting the Horse Before the Cart:A Generator-Evaluator Framework for Question Generation from Text
Automatic question generation (QG) is a useful yet challenging task in NLP.
Recent neural network-based approaches represent the state-of-the-art in this
task. In this work, we attempt to strengthen them significantly by adopting a
holistic and novel generator-evaluator framework that directly optimizes
objectives that reward semantics and structure. The {\it generator} is a
sequence-to-sequence model that incorporates the {\it structure} and {\it
semantics} of the question being generated. The generator predicts an answer in
the passage that the question can pivot on. Employing the copy and coverage
mechanisms, it also acknowledges other contextually important (and possibly
rare) keywords in the passage that the question needs to conform to, while not
redundantly repeating words. The {\it evaluator} model evaluates and assigns a
reward to each predicted question based on its conformity to the {\it
structure} of ground-truth questions. We propose two novel QG-specific reward
functions for text conformity and answer conformity of the generated question.
The evaluator also employs structure-sensitive rewards based on evaluation
measures such as BLEU, GLEU, and ROUGE-L, which are suitable for QG. In
contrast, most of the previous works only optimize the cross-entropy loss,
which can induce inconsistencies between training (objective) and testing
(evaluation) measures. Our evaluation shows that our approach significantly
outperforms state-of-the-art systems on the widely-used SQuAD benchmark as per
both automatic and human evaluation.Comment: 10 pages, The SIGNLL Conference on Computational Natural Language
Learning (CoNLL 2019
Paraphrase Generation with Deep Reinforcement Learning
Automatic generation of paraphrases from a given sentence is an important yet
challenging task in natural language processing (NLP), and plays a key role in
a number of applications such as question answering, search, and dialogue. In
this paper, we present a deep reinforcement learning approach to paraphrase
generation. Specifically, we propose a new framework for the task, which
consists of a \textit{generator} and an \textit{evaluator}, both of which are
learned from data. The generator, built as a sequence-to-sequence learning
model, can produce paraphrases given a sentence. The evaluator, constructed as
a deep matching model, can judge whether two sentences are paraphrases of each
other. The generator is first trained by deep learning and then further
fine-tuned by reinforcement learning in which the reward is given by the
evaluator. For the learning of the evaluator, we propose two methods based on
supervised learning and inverse reinforcement learning respectively, depending
on the type of available training data. Empirical study shows that the learned
evaluator can guide the generator to produce more accurate paraphrases.
Experimental results demonstrate the proposed models (the generators)
outperform the state-of-the-art methods in paraphrase generation in both
automatic evaluation and human evaluation.Comment: EMNLP 201
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