Unveiling Multilinguality in Transformer Models: Exploring Language Specificity in Feed-Forward Networks

Recent research suggests that the feed-forward module within Transformers can be viewed as a collection of key-value memories, where the keys learn to capture specific patterns from the input based on the training examples. The values then combine the output from the 'memories' of the keys to generate predictions about the next token. This leads to an incremental process of prediction that gradually converges towards the final token choice near the output layers. This interesting perspective raises questions about how multilingual models might leverage this mechanism. Specifically, for autoregressive models trained on two or more languages, do all neurons (across layers) respond equally to all languages? No! Our hypothesis centers around the notion that during pretraining, certain model parameters learn strong language-specific features, while others learn more language-agnostic (shared across languages) features. To validate this, we conduct experiments utilizing parallel corpora of two languages that the model was initially pretrained on. Our findings reveal that the layers closest to the network's input or output tend to exhibit more language-specific behaviour compared to the layers in the middle

HUME: Human UCCA-Based Evaluation of Machine Translation

Human evaluation of machine translation normally uses sentence-level measures such as relative ranking or adequacy scales. However, these provide no insight into possible errors, and do not scale well with sentence length. We argue for a semantics-based evaluation, which captures what meaning components are retained in the MT output, thus providing a more fine-grained analysis of translation quality, and enabling the construction and tuning of semantics-based MT. We present a novel human semantic evaluation measure, Human UCCA-based MT Evaluation (HUME), building on the UCCA semantic representation scheme. HUME covers a wider range of semantic phenomena than previous methods and does not rely on semantic annotation of the potentially garbled MT output. We experiment with four language pairs, demonstrating HUME's broad applicability, and report good inter-annotator agreement rates and correlation with human adequacy scores

Results of the WMT15 Metrics Shared Task

This paper presents the results of the WMT15 Metrics Shared Task. We asked participants of this task to score the outputs of the MT systems involved in the WMT15 Shared Translation Task. We collected scores of 46 metrics from 11 research groups. In addition to that, we computed scores of 7 standard metrics (BLEU, SentBLEU, NIST, WER, PER, TER and CDER) as baselines. The collected scores were evaluated in terms of system level correlation (how well each metric's scores correlate with WMT15 official manual ranking of systems) and in terms of segment level correlation (how often a metric agrees with humans in comparing two translations of a particular sentence)

The WMT'18 Morpheval test suites for English-Czech, English-German, English-Finnish and Turkish-English

Peer reviewe

Ten Years of WMT Evaluation Campaigns: Lessons Learnt

The WMT evaluation campaign (http://www.statmt.org/wmt16) has been run annually since 2006. It is a collection of shared tasks related to machine translation, in which researchers compare their techniques against those of others in the field. The longest running task in the campaign is the translation task, where participants translate a common test set with their MT systems. In addition to the translation task, we have also included shared tasks on evaluation: both on automatic metrics (since 2008), which compare the reference to the MT system output, and on quality estimation (since 2012), where system output is evaluated without a reference. An important component of WMT has always been the manual evaluation, wherein human annotators are used to produce the official ranking of the systems in each translation task. This reflects the belief of theWMTorganizers that human judgement should be the ultimate arbiter of MT quality. Over the years, we have experimented with different methods of improving the reliability, efficiency and discriminatory power of these judgements. In this paper we report on our experiences in running this evaluation campaign, the current state of the art in MT evaluation (both human and automatic), and our plans for future editions of WMT

Edinburgh's Statistical Machine Translation Systems for WMT16

This paper describes the University of Edinburgh’s phrase-based and syntax-based submissions to the shared translation tasks of the ACL 2016 First Conference on Machine Translation (WMT16). We submitted five phrase-based and five syntaxbased systems for the news task, plus one phrase-based system for the biomedical task

Moses: Open Source Toolkit for Statistical Machine Translation

We describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c) efficient data formats for translation models and language models. In addition to the SMT decoder, the toolkit also includes a wide variety of tools for training, tuning and applying the system to many translation tasks

Findings of the WMT 2017 Biomedical Translation Shared Task

Automatic translation of documents is an important task in many domains, including the biological and clinical domains. The second edition of the Biomedical Translation task in the Conference of Machine Translation focused on the automatic translation of biomedical-related documents between English and various European languages. This year, we addressed ten languages: Czech, German, English, French, Hungarian, Polish, Portuguese, Spanish, Romanian and Swedish. Test sets included both scientific publications (from the Scielo and EDP Sciences databases) and health-related news (from the Cochrane and UK National Health Service web sites). Seven teams participated in the task, submitting a total of 82 runs. Herein we describe the test sets, participating systems and results of both the automatic and manual evaluation of the translations