6,626 research outputs found
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
Multi-turn Inference Matching Network for Natural Language Inference
Natural Language Inference (NLI) is a fundamental and challenging task in
Natural Language Processing (NLP). Most existing methods only apply one-pass
inference process on a mixed matching feature, which is a concatenation of
different matching features between a premise and a hypothesis. In this paper,
we propose a new model called Multi-turn Inference Matching Network (MIMN) to
perform multi-turn inference on different matching features. In each turn, the
model focuses on one particular matching feature instead of the mixed matching
feature. To enhance the interaction between different matching features, a
memory component is employed to store the history inference information. The
inference of each turn is performed on the current matching feature and the
memory. We conduct experiments on three different NLI datasets. The
experimental results show that our model outperforms or achieves the
state-of-the-art performance on all the three datasets
Entity Synonym Discovery via Multipiece Bilateral Context Matching
Being able to automatically discover synonymous entities in an open-world
setting benefits various tasks such as entity disambiguation or knowledge graph
canonicalization. Existing works either only utilize entity features, or rely
on structured annotations from a single piece of context where the entity is
mentioned. To leverage diverse contexts where entities are mentioned, in this
paper, we generalize the distributional hypothesis to a multi-context setting
and propose a synonym discovery framework that detects entity synonyms from
free-text corpora with considerations on effectiveness and robustness. As one
of the key components in synonym discovery, we introduce a neural network model
SYNONYMNET to determine whether or not two given entities are synonym with each
other. Instead of using entities features, SYNONYMNET makes use of multiple
pieces of contexts in which the entity is mentioned, and compares the
context-level similarity via a bilateral matching schema. Experimental results
demonstrate that the proposed model is able to detect synonym sets that are not
observed during training on both generic and domain-specific datasets:
Wiki+Freebase, PubMed+UMLS, and MedBook+MKG, with up to 4.16% improvement in
terms of Area Under the Curve and 3.19% in terms of Mean Average Precision
compared to the best baseline method.Comment: In IJCAI 2020 as a long paper. Code and data are available at
https://github.com/czhang99/SynonymNe
Neural Paraphrase Identification of Questions with Noisy Pretraining
We present a solution to the problem of paraphrase identification of
questions. We focus on a recent dataset of question pairs annotated with binary
paraphrase labels and show that a variant of the decomposable attention model
(Parikh et al., 2016) results in accurate performance on this task, while being
far simpler than many competing neural architectures. Furthermore, when the
model is pretrained on a noisy dataset of automatically collected question
paraphrases, it obtains the best reported performance on the dataset
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