47 research outputs found
Knowledge base question answering with a matching-aggregation model and question-specific contextual relations
National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ
Multi-level head-wise match and aggregation in transformer for textual sequence matching
Transformer has been successfully applied to many natural language processing
tasks. However, for textual sequence matching, simple matching between the
representation of a pair of sequences might bring in unnecessary noise. In this
paper, we propose a new approach to sequence pair matching with Transformer, by
learning head-wise matching representations on multiple levels. Experiments
show that our proposed approach can achieve new state-of-the-art performance on
multiple tasks that rely only on pre-computed sequence-vector-representation,
such as SNLI, MNLI-match, MNLI-mismatch, QQP, and SQuAD-binary.Comment: AAAI 2020, 8 page
Learning natural language inference with LSTM
Natural language inference (NLI) is a fundamentally important task in natural
language processing that has many applications. The recently released Stanford
Natural Language Inference (SNLI) corpus has made it possible to develop and
evaluate learning-centered methods such as deep neural networks for natural
language inference (NLI). In this paper, we propose a special long short-term
memory (LSTM) architecture for NLI. Our model builds on top of a recently
proposed neural attention model for NLI but is based on a significantly
different idea. Instead of deriving sentence embeddings for the premise and the
hypothesis to be used for classification, our solution uses a match-LSTM to
perform word-by-word matching of the hypothesis with the premise. This LSTM is
able to place more emphasis on important word-level matching results. In
particular, we observe that this LSTM remembers important mismatches that are
critical for predicting the contradiction or the neutral relationship label. On
the SNLI corpus, our model achieves an accuracy of 86.1%, outperforming the
state of the art.Comment: 10 pages, 2 figure
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