Optimising Multiple Metrics with MERT

International audienceThe main metric used for SMT systems evaluation an optimisation is BLEU score but this metric is questioned about its relevance to human evaluation. Some other metrics already exist but none of them are in perfect harmony with human evaluation. On the other hand, most evaluations use multiple metrics (BLEU, TER, METEOR, etc.). Systems can optimise toward other metrics than BLEU. But optimisation with other metrics tends to decrease BLEU score. As Machine Translation evaluations still use BLEU as main metric, it is important to min-imise the decrease of BLEU. We propose to optimise toward a metric combination like BLEU-TER. This proposition includes two new open source scorers for MERT, the SMT optimisation tool. The first one is a TER scorer that allows us to optimise toward TER; the second one is a combination scorer. The latter one enables the combination of two or more metrics for the optimisation process. This paper also presents some experiments on the MERT optimisation in the Statistical Machine Translation system Moses with the TER and the BLEU metrics and some metric combinations

Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond

We introduce an architecture to learn joint multilingual sentence representations for 93 languages, belonging to more than 30 different language families and written in 28 different scripts. Our system uses a single BiLSTM encoder with a shared BPE vocabulary for all languages, which is coupled with an auxiliary decoder and trained on publicly available parallel corpora. This enables us to learn a classifier on top of the resulting sentence embeddings using English annotated data only, and transfer it to any of the 93 languages without any modification. Our approach sets a new state-of-the-art on zero-shot cross-lingual natural language inference for all the 14 languages in the XNLI dataset but one. We also achieve very competitive results in cross-lingual document classification (MLDoc dataset). Our sentence embeddings are also strong at parallel corpus mining, establishing a new state-of-the-art in the BUCC shared task for 3 of its 4 language pairs. Finally, we introduce a new test set of aligned sentences in 122 languages based on the Tatoeba corpus, and show that our sentence embeddings obtain strong results in multilingual similarity search even for low-resource languages. Our PyTorch implementation, pre-trained encoder and the multilingual test set will be freely available

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder-Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.Comment: EMNLP 201

Large and Diverse Language Models for Statistical Machine Translation

International audienceno abstrac

Qualitative analysis of post-editing for high quality machine translation

In the context of massive adoption of Machine Translation (MT) by human localization services in Post-Editing (PE) workflows, we analyze the activity of post-editing high quality translations through a novel PE analysis methodology. We define and introduce a new unit for evaluating post-editing effort based on Post-Editing Action (PEA) - for which we provide human evaluation guidelines and propose a process to automatically evaluate these PEAs. We applied this methodology on data sets from two technologically different MT systems. In that context, we could show that more than 35% of the remaining effort can be saved by introducing of global PEA and edit propagation