Neural Natural Language Inference Models Enhanced with External Knowledge

Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.Comment: Accepted by ACL 201

Evaluation of word embeddings against cognitive processes: primed reaction times in lexical decision and naming tasks

International audienceThis work presents a framework for word similarity evaluation grounded on cogni-tive sciences experimental data. Word pair similarities are compared to reaction times of subjects in large scale lexical decision and naming tasks under semantic priming. Results show that GloVe embeddings lead to significantly higher correlation with experimental measurements than other controlled and off-the-shelf embeddings, and that the choice of a training corpus is less important than that of the algorithm. Comparison of rankings with other datasets shows that the cognitive phenomenon covers more aspects than simply word related-ness or similarity

Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference

This paper presents a new deep learning architecture for Natural Language Inference (NLI). Firstly, we introduce a new architecture where alignment pairs are compared, compressed and then propagated to upper layers for enhanced representation learning. Secondly, we adopt factorization layers for efficient and expressive compression of alignment vectors into scalar features, which are then used to augment the base word representations. The design of our approach is aimed to be conceptually simple, compact and yet powerful. We conduct experiments on three popular benchmarks, SNLI, MultiNLI and SciTail, achieving competitive performance on all. A lightweight parameterization of our model also enjoys a $\approx 3$ times reduction in parameter size compared to the existing state-of-the-art models, e.g., ESIM and DIIN, while maintaining competitive performance. Additionally, visual analysis shows that our propagated features are highly interpretable.Comment: EMNLP 2018 CRC and Update CAFE + ELMo result on SNL

LCT-MALTAs submission to RepEval 2017 shared task

We present in this paper our team LCTMALTA’s submission to the RepEval 2017 Shared Task on natural language inference. Our system is a simple system based on a standard BiLSTM architecture, using as input GloVe word embeddings augmented with further linguistic information. We use max pooling on the BiLSTM outputs to obtain embeddings for sentences. On both the matched and the mismatched test sets, our system clearly beats the shared task’s BiLSTM baseline model.peer-reviewe

e-SNLI: Natural Language Inference with Natural Language Explanations

In order for machine learning to garner widespread public adoption, models must be able to provide interpretable and robust explanations for their decisions, as well as learn from human-provided explanations at train time. In this work, we extend the Stanford Natural Language Inference dataset with an additional layer of human-annotated natural language explanations of the entailment relations. We further implement models that incorporate these explanations into their training process and output them at test time. We show how our corpus of explanations, which we call e-SNLI, can be used for various goals, such as obtaining full sentence justifications of a model's decisions, improving universal sentence representations and transferring to out-of-domain NLI datasets. Our dataset thus opens up a range of research directions for using natural language explanations, both for improving models and for asserting their trust.Comment: NeurIPS 201