Articles

AbstractTo comprehend an argument and fill the gap between claims and reasons, it is vital to find the implicit supporting warrants behind. In this paper, we propose a hierarchical attention model to identify the right warrant which explains why the reason stands for the claim. Our model focuses not only …...

AbstractThe Argument Reasoning Comprehension Task is a difficult challenge requiring significant language understanding and complex reasoning over world knowledge. We focus on transfer of a sentence encoder to bootstrap more complicated architectures given the small size of the dataset. Our best model uses a pre-trained BiLSTM to encode input sentences, …...

AbstractThis paper presents our submissions to SemEval 2018 Task 12: the Argument Reasoning Comprehension Task. We investigate an end-to-end attention-based neural network to represent the two lexically close candidate warrants. On the one hand, we extract their different parts as attention vectors to obtain distinguishable representations. On the other hand, …...

AbstractReasoning is a very important topic and has many important applications in the field of natural language processing. Semantic Evaluation (SemEval) 2018 Task 12 “The Argument Reasoning Comprehension” committed to research natural language reasoning. In this task, we proposed a novel argument reasoning comprehension system, ITNLP-ARC, which use Neural Networks …...

AbstractThis paper describes MITRE’s participation in SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. The techniques explored range from simple bag-of-ngrams classifiers to neural architectures with varied attention and alignment mechanisms. Logistic regression ties the systems together into an ensemble submitted for evaluation. The resulting system answers reading comprehension questions …...

SGNMT is a decoding platform for machine translation which allows paring various modern neural models of translation with different kinds of constraints and symbolic models. In this paper, we describe three use cases in which SGNMT is currently playing an active role: (1) teaching as SGNMT is being used for …...

This paper describes XNMT, the eXtensible Neural Machine Translation toolkit. XNMT distin- guishes itself from other open-source NMT toolkits by its focus on modular code design, with the purpose of enabling fast iteration in research and replicable, reliable results. In this paper we describe the design of XNMT and its …...

OpenNMT is an open-source toolkit for neural machine translation (NMT). The system prior- itizes efficiency, modularity, and extensibility with the goal of supporting NMT research into model architectures, feature representations, and source modalities, while maintaining compet- itive performance and reasonable training requirements. The toolkit consists of modeling and translation support, …...

Low-resource machine translation is a challenging problem, especially when the source lan- guage is morphologically complex. We describe a simple procedure for constructing glosses, or mappings between complex, inflected source-language words and equivalent multi-word English expressions. We demonstrate the utility of glosses, especially compared to entries in bilingual dictionaries, across …...

Neural machine translation systems have been shown to achieve state-of-the-art translation per- formance for many language pairs. In order to produce a correct translation, MT systems must learn how to disambiguate words with multiple senses and pick the correct translation. We ex- plore the extent to which the word embeddings …...

We describe SOCKEYE,1 an open-source sequence-to-sequence toolkit for Neural Machine Translation (NMT). SOCKEYE is a production-ready framework for training and applying models as well as an experimental platform for researchers. Written in Python and built on MXNET, the toolkit offers scalable training and inference for the three most prominent encoder- …...

Superiority of neural machine translation (NMT) and phrase-based statistical machine transla- tion (PBSMT) depends on the translation task. For some translation tasks, such as those involv- ing low-resource language pairs or close languages, NMT may underperform PBSMT. In order to have a translation system that performs consistently better regardless of …...

Neural machine translation (NMT) models are conventionally trained with fixed-size vocabu- laries to control the computational complexity and the quality of the learned word represen- tations. This, however, limits the accuracy and the generalization capability of the models, especially for morphologically-rich languages, which usually have very sparse vocabularies containing rare …...

This paper describes an approach for translation between Chinese dialects that can produce target sentences at different registers. We focus on Mandarin as the source language, and Cantonese as the target. Mutually unintelligible, these two varieties of Chinese exhibit dif- ferences at both the lexical and syntactic levels, and the …...

Neural Monkey is an open-source toolkit for sequence-to-sequence learning. The focus of this paper is to present the current state of the toolkit to the intended audience, which includes students and researchers, both active in the deep learning community and newcomers. For each of these target groups, we describe the …...

Social media platforms such as Twitter and Facebook are hugely popular websites through which Internet users can communicate and spread information worldwide. On Twitter, mes- sages (tweets) are generated by users from all over the world in many different languages. Tweets about different events almost always encode some degree of …...

This paper investigates the robustness of NLP against perturbed word forms. While neural approaches can achieve (almost) human-like accuracy for certain tasks and conditions, they often are sensitive to small changes in the input such as non-canonical input (e.g., typos). Yet both stability and robustness are desired properties in applications …...

Out-of-vocabulary word translation is a major problem for the translation of low-resource lan- guages that suffer from a lack of parallel training data. This paper evaluates the contributions of target-language context models towards the translation of OOV words, specifically in those cases where OOV translations are derived from external knowledge …...

Translation to morphologically rich languages is a difficult task because of sparsity caused by morphological richness. In this work we perform a pilot study on predicting the morphologi- cally rich POS tags of sequences of lemmas. Similar studies have been conducted in the context of phrase-based statistical machine translation. We …...

We investigate different strategies for combining quality estimation (QE) and automatic post- editing (APE) to improve the output of machine translation (MT) systems. The joint contribu- tion of the two technologies is analyzed in different settings, in which QE serves as either: i) an activator of APE corrections, or ii) …...