31,140 research outputs found
A compare-aggregate model for matching text sequences
Many NLP tasks including machine comprehension, answer selection and text
entailment require the comparison between sequences. Matching the important
units between sequences is a key to solve these problems. In this paper, we
present a general "compare-aggregate" framework that performs word-level
matching followed by aggregation using Convolutional Neural Networks. We
particularly focus on the different comparison functions we can use to match
two vectors. We use four different datasets to evaluate the model. We find that
some simple comparison functions based on element-wise operations can work
better than standard neural network and neural tensor network.Comment: 11 pages, 2 figure
Bilateral Multi-Perspective Matching for Natural Language Sentences
Natural language sentence matching is a fundamental technology for a variety
of tasks. Previous approaches either match sentences from a single direction or
only apply single granular (word-by-word or sentence-by-sentence) matching. In
this work, we propose a bilateral multi-perspective matching (BiMPM) model
under the "matching-aggregation" framework. Given two sentences and ,
our model first encodes them with a BiLSTM encoder. Next, we match the two
encoded sentences in two directions and . In
each matching direction, each time step of one sentence is matched against all
time-steps of the other sentence from multiple perspectives. Then, another
BiLSTM layer is utilized to aggregate the matching results into a fix-length
matching vector. Finally, based on the matching vector, the decision is made
through a fully connected layer. We evaluate our model on three tasks:
paraphrase identification, natural language inference and answer sentence
selection. Experimental results on standard benchmark datasets show that our
model achieves the state-of-the-art performance on all tasks.Comment: To appear in Proceedings of IJCAI 201
Structured Review of Code Clone Literature
This report presents the results of a structured review of code clone literature. The aim of the review is to assemble a conceptual model of clone-related concepts which helps us to reason about clones. This conceptual model unifies clone concepts from a wide range of literature, so that findings about clones can be compared with each other
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