Pretrained character-level and byte-level language models have been shown to be competitive with popular subword models across a range of Natural Language Processing tasks. However, there has been little research on their effectiveness for neural machine translation (NMT), particularly within the popular pretrain-then-finetune paradigm. This work performs an extensive comparison across multiple languages and experimental conditions of character- and subword-level pretrained models (ByT5 and mT5, respectively) on NMT. We show the effectiveness of character-level modeling in translation, particularly in cases where fine-tuning data is limited. In our analysis, we show how character models’ gains in translation quality are reflected in better translations of orthographically similar words and rare words. While evaluating the importance of source texts in driving model predictions, we highlight word-level patterns within ByT5, suggesting an ability to modulate word-level and character-level information during generation. We conclude by assessing the efficiency tradeoff of byte models, suggesting their usage in non-time-critical scenarios to boost translation quality.

The effectiveness of neural machine translation is markedly constrained in low-resource scenarios, where the scarcity of parallel data hampers the development of robust models. This paper focuses on the scenario where the source language is low-resourceand there exists a related high-resource language, for which we introduce a novel approach that combines pivot translation and multilingual training. As a use case we tackle the automatic translation from Catalan to Chinese, using Spanish as an additional language. Our evaluation, conducted on the FLORES-200 benchmark, compares our new approach against a vanilla baseline alongside other models representing various low-resource techniques in the Catalan-to-Chinese context. Experimental results highlight the efficacy of our proposed method, which outperforms existing models, notably demonstrating significant improvements both in translation quality and in lexical diversity.

Machine translations are found to be lexically poorer than human translations. The loss of lexical diversity through MT poses an issue in the automatic translation of litrature, where it matters not only what is written, but also how it is written. Current methods for increasing lexical diversity in MT are rigid. Yet, as we demonstrate, the degree of lexical diversity can vary considerably across different novels. Thus, rather than aiming for the rigid increase of lexical diversity, we reframe the task as recovering what is lost in the machine translation process. We propose a novel approach that consists of reranking translation candidates with a classifier that distinguishes between original and translated text. We evaluate our approach on 31 English-to-Dutch book translations, and find that, for certain books, our approach retrieves lexical diversity scores that are close to human translation.

LT-LiDER is an Erasmus+ cooperation project with two main aims. The first is to map the landscape of technological capabilities required to work as a language and/or translation expert in the digitalised and datafied language industry. The second is to generate training outputs that will help language and translation trainers improve their skills and adopt appropriate pedagogical approaches and strategies for integrating data-driven technology into their language or translation classrooms, with a focus on digital and AI literacy.

In the context of the ReproHum project aimed at assessing the reliability of human evaluation, we replicated the human evaluation conducted in “Generating Scientific Definitions with Controllable Complexity” by August et al. (2022). Specifically, humans were asked to assess the fluency of automatically generated scientific definitions by three different models, with output complexity varying according to target audience. Evaluation conditions were kept as close as possible to the original study, except of necessary and minor adjustments. Our results, despite yielding lower absolute performance, show that relative performance across the three tested systems remains comparable to what was observed in the original paper. On the basis of lower inter-annotator agreement and feedback received from annotators in our experiment, we also observe that the ambiguity of the concept being evaluated may play a substantial role in human assessment.

Large, curated, web-crawled corpora play a vital role in training language models (LMs). They form the lion’s share of the training data in virtually all recent LMs, such as the well-known GPT, LLaMA and XLM-RoBERTa models. However, despite this importance, relatively little attention has been given to the quality of these corpora. In this paper, we compare four of the currently most relevant large, web-crawled corpora (CC100, MaCoCu, mC4 and OSCAR) across eleven lower-resourced European languages. Our approach is two-fold: first, we perform an intrinsic evaluation by performing a human evaluation of the quality of samples taken from different corpora; then, we assess the practical impact of the qualitative differences by training specific LMs on each of the corpora and evaluating their performance on downstream tasks. We find that there are clear differences in quality of the corpora, with MaCoCu and OSCAR obtaining the best results. However, during the extrinsic evaluation, we actually find that the CC100 corpus achieves the highest scores. We conclude that, in our experiments, the quality of the web-crawled corpora does not seem to play a significant role when training LMs.

Figures of speech help people express abstract concepts and evoke stronger emotions than literal expressions, thereby making texts more creative and engaging. Due to its pervasive and fundamental character, figurative language understanding has been addressed in Natural Language Processing, but it’s highly understudied in a multilingual setting and when considering more than one figure of speech at the same time. To bridge this gap, we introduce multilingual multi-figurative language modelling, and provide a benchmark for sentence-level figurative language detection, covering three common figures of speech and seven languages. Specifically, we develop a framework for figurative language detection based on template-based prompt learning. In so doing, we unify multiple detection tasks that are interrelated across multiple figures of speech and languages, without requiring task- or language-specific modules. Experimental results show that our framework outperforms several strong baselines and may serve as a blueprint for the joint modelling of other interrelated tasks.

We reproduced the human-based evaluation of the continuation of narratives task presented by Chakrabarty et al. (2022). This experiment is performed as part of the ReproNLP Shared Task on Reproducibility of Evaluations in NLP (Track C). Our main goal is to reproduce the original study under conditions as similar as possible. Specifically, we follow the original experimental design and perform human evaluations of the data from the original study, while describing the differences between the two studies. We then present the results of these two studies together with an analysis of similarities between them. Inter-annotator agreement (Krippendorff’s alpha) in the reproduction study is lower than in the original study, while the human evaluation results of both studies have the same trends, that is, our results support the findings in the original study.

We tackle the task of automatically discriminating between human and machine translations. As opposed to most previous work, we perform experiments in a multilingual setting, considering multiple languages and multilingual pretrained language models. We show that a classifier trained on parallel data with a single source language (in our case German–English) can still perform well on English translations that come from different source languages, even when the machine translations were produced by other systems than the one it was trained on. Additionally, we demonstrate that incorporating the source text in the input of a multilingual classifier improves (i) its accuracy and (ii) its robustness on cross-system evaluation, compared to a monolingual classifier. Furthermore, we find that using training data from multiple source languages (German, Russian and Chinese) tends to improve the accuracy of both monolingual and multilingual classifiers. Finally, we show that bilingual classifiers and classifiers trained on multiple source languages benefit from being trained on longer text sequences, rather than on sentences.

We present the most relevant results of the project MaCoCu: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages in its second year. To date, parallel and monolingual corpora have been produced for seven low-resourced European languages by crawling large amounts of textual data from selected top-level domains of the Internet; both human and automatic evaluation show its usefulness. In addition, several large language models pretrained on MaCoCu data have been published, as well as the code used to collect and curate the data.

We report our efforts in identifying a set of previous human evaluations in NLP that would be suitable for a coordinated study examining what makes human evaluations in NLP more/less reproducible. We present our results and findings, which include that just 13% of papers had (i) sufficiently low barriers to reproduction, and (ii) enough obtainable information, to be considered for reproduction, and that all but one of the experiments we selected for reproduction was discovered to have flaws that made the meaningfulness of conducting a reproduction questionable. As a result, we had to change our coordinated study design from a reproduce approach to a standardise-then-reproduce-twice approach. Our overall (negative) finding that the great majority of human evaluations in NLP is not repeatable and/or not reproducible and/or too flawed to justify reproduction, paints a dire picture, but presents an opportunity for a rethink about how to design and report human evaluations in NLP.

Character identification is a key element for many narrative-related tasks. To implement it, the baseform of the name of the character (or lemma) needs to be identified, so different appearances of the same character in the narrative could be aligned. In this paper we tackle this problem in translated texts (English–Finnish translation direction), where the challenge regarding lemmatizing foreign names in an agglutinative language appears. To solve this problem, we present and compare several methods. The results show that the method based on a search for the shortest version of the name proves to be the easiest, best performing (83.4% F1), and most resource-independent.

We exploit the pre-trained seq2seq model mBART for multilingual text style transfer. Using machine translated data as well as gold aligned English sentences yields state-of-the-art results in the three target languages we consider. Besides, in view of the general scarcity of parallel data, we propose a modular approach for multilingual formality transfer, which consists of two training strategies that target adaptation to both language and task. Our approach achieves competitive performance without monolingual task-specific parallel data and can be applied to other style transfer tasks as well as to other languages.

Although text style transfer has witnessed rapid development in recent years, there is as yet no established standard for evaluation, which is performed using several automatic metrics, lacking the possibility of always resorting to human judgement. We focus on the task of formality transfer, and on the three aspects that are usually evaluated: style strength, content preservation, and fluency. To cast light on how such aspects are assessed by common and new metrics, we run a human-based evaluation and perform a rich correlation analysis. We are then able to offer some recommendations on the use of such metrics in formality transfer, also with an eye to their generalisability (or not) to related tasks.

We introduce DivEMT, the first publicly available post-editing study of Neural Machine Translation (NMT) over a typologically diverse set of target languages. Using a strictly controlled setup, 18 professional translators were instructed to translate or post-edit the same set of English documents into Arabic, Dutch, Italian, Turkish, Ukrainian, and Vietnamese. During the process, their edits, keystrokes, editing times and pauses were recorded, enabling an in-depth, cross-lingual evaluation of NMT quality and post-editing effectiveness. Using this new dataset, we assess the impact of two state-of-the-art NMT systems, Google Translate and the multilingual mBART-50 model, on translation productivity. We find that post-editing is consistently faster than translation from scratch. However, the magnitude of productivity gains varies widely across systems and languages, highlighting major disparities in post-editing effectiveness for languages at different degrees of typological relatedness to English, even when controlling for system architecture and training data size. We publicly release the complete dataset including all collected behavioral data, to foster new research on the translation capabilities of NMT systems for typologically diverse languages.

Subword-level models have been the dominant paradigm in NLP. However, character-level models have the benefit of seeing each character individually, providing the model with more detailed information that ultimately could lead to better models. Recent works have shown character-level models to be competitive with subword models, but costly in terms of time and computation. Character-level models with a downsampling component alleviate this, but at the cost of quality, particularly for machine translation. This work analyzes the problems of previous downsampling methods and introduces a novel downsampling method which is informed by subwords.This new downsampling method not only outperforms existing downsampling methods, showing that downsampling characters can be done without sacrificing quality, but also leads to promising performance compared to subword models for translation.

An important goal of the MaCoCu project is to improve EU-specific NLP systems that concern their Digital Service Infrastructures (DSIs). In this paper we aim at boosting the creation of such domain-specific NLP systems. To do so, we explore the feasibility of building an automatic classifier that allows to identify which segments in a generic (potentially parallel) corpus are relevant for a particular DSI. We create an evaluation data set by crawling DSI-specific web domains and then compare different strategies to build our DSI classifier for text in three languages: English, Spanish and Dutch. We use pre-trained (multilingual) language models to perform the classification, with zero-shot classification for Spanish and Dutch. The results are promising, as we are able to classify DSIs with between 70 and 80% accuracy, even without in-language training data. A manual annotation of the data revealed that we can also find DSI-specific data on crawled texts from general web domains with reasonable accuracy. We publicly release all data, predictions and code, as to allow future investigations in whether exploiting this DSI-specific data actually leads to improved performance on particular applications, such as machine translation.

We address the task of automatically distinguishing between human-translated (HT) and machine translated (MT) texts. Following recent work, we fine-tune pre-trained language models (LMs) to perform this task. Our work differs in that we use state-of-the-art pre-trained LMs, as well as the test sets of the WMT news shared tasks as training data, to ensure the sentences were not seen during training of the MT system itself. Moreover, we analyse performance for a number of different experimental setups, such as adding translationese data, going beyond the sentence-level and normalizing punctuation. We show that (i) choosing a state-of-the-art LM can make quite a difference: our best baseline system (DeBERTa) outperforms both BERT and RoBERTa by over 3% accuracy, (ii) adding translationese data is only beneficial if there is not much data available, (iii) considerable improvements can be obtained by classifying at the document-level and (iv) normalizing punctuation and thus avoiding (some) shortcuts has no impact on model performance.

We introduce the project “MaCoCu: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages”, funded by the Connecting Europe Facility, which is aimed at building monolingual and parallel corpora for under-resourced European languages. The approach followed consists of crawling large amounts of textual data from carefully selected top-level domains of the Internet, and then applying a curation and enrichment pipeline. In addition to corpora, the project will release successive versions of the free/open-source web crawling and curation software used.

We present here the EU-funded project CREAMT that seeks to understand what is meant by creativity in different translation modalities, e.g. machine translation, post-editing or professional translation. Focusing on the textual elements that determine creativity in translated literary texts and the reader experience, CREAMT uses a novel, interdisciplinary approach to assess how effective MT is in literary translation considering creativity in translation and the ultimate user: the reader.

Most of the work on Character Networks to date is limited to monolingual texts. Conversely, in this paper we apply and analyze Character Networks on both source texts (English novels) and their Finnish translations (both human- and machine-translated). We assume that this analysis could provide some insights on changes in translations that could modify the character networks, as well as the narrative. The results show that the character networks of translations differ from originals in case of long novels, and the differences may also vary depending on the novel and translator’s strategy.

This paper investigates very low resource language model pretraining, when less than 100 thousand sentences are available. We find that, in very low-resource scenarios, statistical n-gram language models outperform state-of-the-art neural models. Our experiments show that this is mainly due to the focus of the former on a local context. As such, we introduce three methods to improve a neural model’s performance in the low-resource setting, finding that limiting the model’s self-attention is the most effective one, improving on downstream tasks such as NLI and POS tagging by up to 5% for the languages we test on: English, Hindi, and Turkish.

Scarcity of parallel data causes formality style transfer models to have scarce success in preserving content. We show that fine-tuning pre-trained language (GPT-2) and sequence-to-sequence (BART) models boosts content preservation, and that this is possible even with limited amounts of parallel data. Augmenting these models with rewards that target style and content –the two core aspects of the task– we achieve a new state-of-the-art.

This paper describes the methods behind the systems submitted by the University of Groningen for the WMT 2021 Unsupervised Machine Translation task for German–Lower Sorbian (DE–DSB): a high-resource language to a low-resource one. Our system uses a transformer encoder-decoder architecture in which we make three changes to the standard training procedure. First, our training focuses on two languages at a time, contrasting with a wealth of research on multilingual systems. Second, we introduce a novel method for initializing the vocabulary of an unseen language, achieving improvements of 3.2 BLEU for DE->DSB and 4.0 BLEU for DSB->DE.Lastly, we experiment with the order in which offline and online back-translation are used to train an unsupervised system, finding that using online back-translation first works better for DE->DSB by 2.76 BLEU. Our submissions ranked first (tied with another team) for DSB->DE and third for DE->DSB.

Style transfer aims to rewrite a source text in a different target style while preserving its content. We propose a novel approach to this task that leverages generic resources, and without using any task-specific parallel (source–target) data outperforms existing unsupervised approaches on the two most popular style transfer tasks: formality transfer and polarity swap. In practice, we adopt a multi-step procedure which builds on a generic pre-trained sequence-to-sequence model (BART). First, we strengthen the model’s ability to rewrite by further pre-training BART on both an existing collection of generic paraphrases, as well as on synthetic pairs created using a general-purpose lexical resource. Second, through an iterative back-translation approach, we train two models, each in a transfer direction, so that they can provide each other with synthetically generated pairs, dynamically in the training process. Lastly, we let our best resulting model generate static synthetic pairs to be used in a supervised training regime. Besides methodology and state-of-the-art results, a core contribution of this work is a reflection on the nature of the two tasks we address, and how their differences are highlighted by their response to our approach.

Translating to and from low-resource polysynthetic languages present numerous challenges for NMT. We present the results of our systems for the English–Inuktitut language pair for the WMT 2020 translation tasks. We investigated the importance of correct morphological segmentation, whether or not adding data from a related language (Greenlandic) helps, and whether using contextual word embeddings improves translation. While each method showed some promise, the results are mixed.

This paper describes the methods behind the systems submitted by the University of Groningen for the WMT 2020 Unsupervised Machine Translation task for German–Upper Sorbian. We investigate the usefulness of data selection in the unsupervised setting. We find that we can perform data selection using a pretrained model and show that the quality of a set of sentences or documents can have a great impact on the performance of the UNMT system trained on it. Furthermore, we show that document-level data selection should be preferred for training the XLM model when possible. Finally, we show that there is a trade-off between quality and quantity of the data used to train UNMT systems.

Unsupervised Machine Translation has been advancing our ability to translate without parallel data, but state-of-the-art methods assume an abundance of monolingual data. This paper investigates the scenario where monolingual data is limited as well, finding that current unsupervised methods suffer in performance under this stricter setting. We find that the performance loss originates from the poor quality of the pretrained monolingual embeddings, and we offer a potential solution: dependency-based word embeddings. These embeddings result in a complementary word representation which offers a boost in performance of around 1.5 BLEU points compared to standard word2vec when monolingual data is limited to 1 million sentences per language. We also find that the inclusion of sub-word information is crucial to improving the quality of the embeddings.

This research presents a fine-grained human evaluation to compare the Transformer and recurrent approaches to neural machine translation (MT), on the translation direction English-to-Chinese. To this end, we develop an error taxonomy compliant with the Multidimensional Quality Metrics (MQM) framework that is customised to the relevant phenomena of this translation direction. We then conduct an error annotation using this customised error taxonomy on the output of state-of-the-art recurrent- and Transformer-based MT systems on a subset of WMT2019’s news test set. The resulting annotation shows that, compared to the best recurrent system, the best Transformer system results in a 31% reduction of the total number of errors and it produced significantly less errors in 10 out of 22 error categories. We also note that two of the systems evaluated do not produce any error for a category that was relevant for this translation direction prior to the advent of NMT systems: Chinese classifiers.

We reassess the claims of human parity and super-human performance made at the news shared task of WMT2019 for three translation directions: English→German, English→Russian and German→English. First we identify three potential issues in the human evaluation of that shared task: (i) the limited amount of intersen- tential context available, (ii) the limited translation proficiency of the evaluators and (iii) the use of a reference transla- tion. We then conduct a modified eval- uation taking these issues into account. Our results indicate that all the claims of human parity and super-human perfor- mance made at WMT2019 should be re- futed, except the claim of human parity for English→German. Based on our findings, we put forward a set of recommendations and open questions for future assessments of human parity in machine translation.

We combine character-level and contextual language model representations to improve performance on Discourse Representation Structure parsing. Character representations can easily be added in a sequence-to-sequence model in either one encoder or as a fully separate encoder, with improvements that are robust to different language models, languages and data sets. For English, these improvements are larger than adding individual sources of linguistic information or adding non-contextual embeddings. A new method of analysis based on semantic tags demonstrates that the character-level representations improve performance across a subset of selected semantic phenomena.

Recently, sequence-to-sequence models have achieved impressive performance on a number of semantic parsing tasks. However, they often do not exploit available linguistic resources, while these, when employed correctly, are likely to increase performance even further. Research in neural machine translation has shown that employing this information has a lot of potential, especially when using a multi-encoder setup. We employ a range of semantic and syntactic resources to improve performance for the task of Discourse Representation Structure Parsing. We show that (i) linguistic features can be beneficial for neural semantic parsing and (ii) the best method of adding these features is by using multiple encoders.

The effect of translationese has been studied in the field of machine translation (MT), mostly with respect to training data. We study in depth the effect of translationese on test data, using the test sets from the last three editions of WMT’s news shared task, containing 17 translation directions. We show evidence that (i) the use of translationese in test sets results in inflated human evaluation scores for MT systems; (ii) in some cases system rankings do change and (iii) the impact translationese has on a translation direction is inversely correlated to the translation quality attainable by state-of-the-art MT systems for that direction.

This paper presents the systems submitted by the University of Groningen to the English– Kazakh language pair (both translation directions) for the WMT 2019 news translation task. We explore the potential benefits of (i) morphological segmentation (both unsupervised and rule-based), given the agglutinative nature of Kazakh, (ii) data from two additional languages (Turkish and Russian), given the scarcity of English–Kazakh data and (iii) synthetic data, both for the source and for the target language. Our best submissions ranked second for Kazakh→English and third for English→Kazakh in terms of the BLEU automatic evaluation metric.

The tutorial will introduce a set of very useful statistical tests for conducting analyses in the research areas of Machine Translation (MT) and Translation Studies (TS). For each statistical test, the presenter will: 1) introduce it in the context of a common research example that pertains to the area of MT and/or TS 2) explain the technique behind the test and its assumptions 3) cover common pitfalls when the test is applied in research studies, and 4) conduct a hands-on activity so that attendees can put the knowledge acquired in practice straight-away. All examples and exercises will be in R. The following statistical tests will be covered: t-tests (both parametric and non-parametric), bootstrap resampling, Pearson and Spearman correlation coefficients, linear mixed-effects models.

Neural methods have had several recent successes in semantic parsing, though they have yet to face the challenge of producing meaning representations based on formal semantics. We present a sequence-to-sequence neural semantic parser that is able to produce Discourse Representation Structures (DRSs) for English sentences with high accuracy, outperforming traditional DRS parsers. To facilitate the learning of the output, we represent DRSs as a sequence of flat clauses and introduce a method to verify that produced DRSs are well-formed and interpretable. We compare models using characters and words as input and see (somewhat surprisingly) that the former performs better than the latter. We show that eliminating variable names from the output using De Bruijn indices increases parser performance. Adding silver training data boosts performance even further.

Given (i) the rise of a new paradigm to machine translation based on neural networks that results in more fluent and less literal output than previous models and (ii) the maturity of machine-assisted translation via post-editing in industry, project PiPeNovel studies the feasibility of the post-editing workflow for literary text conducting experiments with professional literary translators.

We reassess a recent study (Hassan et al., 2018) that claimed that machine translation (MT) has reached human parity for the translation of news from Chinese into English, using pairwise ranking and considering three variables that were not taken into account in that previous study: the language in which the source side of the test set was originally written, the translation proficiency of the evaluators, and the provision of inter-sentential context. If we consider only original source text (i.e. not translated from another language, or translationese), then we find evidence showing that human parity has not been achieved. We compare the judgments of professional translators against those of non-experts and discover that those of the experts result in higher inter-annotator agreement and better discrimination between human and machine translations. In addition, we analyse the human translations of the test set and identify important translation issues. Finally, based on these findings, we provide a set of recommendations for future human evaluations of MT.

We aim to shed light on the strengths and weaknesses of the newly introduced neural machine translation paradigm. To that end, we conduct a multifaceted evaluation in which we compare outputs produced by state-of-the-art neural machine translation and phrase-based machine translation systems for 9 language directions across a number of dimensions. Specifically, we measure the similarity of the outputs, their fluency and amount of reordering, the effect of sentence length and performance across different error categories. We find out that translations produced by neural machine translation systems are considerably different, more fluent and more accurate in terms of word order compared to those produced by phrase-based systems. Neural machine translation systems are also more accurate at producing inflected forms, but they perform poorly when translating very long sentences.

We introduce TweetMT, a parallel corpus of tweets in four language pairs that combine five languages (Spanish from/to Basque, Catalan, Galician and Portuguese), all of which have an official status in the Iberian Peninsula. The corpus has been created by combining automatic collection and crowdsourcing approaches, and it is publicly available. It is intended for the development and testing of microtext machine translation systems. In this paper we describe the methodology followed to build the corpus, and present the results of the shared task in which it was tested.

This paper presents an approach for building large monolingual corpora and, at the same time, extracting parallel data by crawling the top-level domain of a given language of interest. For gathering linguistically relevant data from top-level domains we use the SpiderLing crawler, modified to crawl data written in multiple languages. The output of this process is then fed to Bitextor, a tool for harvesting parallel data from a collection of documents. We call the system combining these two tools Spidextor, a blend of the names of its two crucial parts. We evaluate the described approach intrinsically by measuring the accuracy of the extracted bitexts from the Croatian top-level domain “.hr” and the Slovene top-level domain “.si”, and extrinsically on the English-Croatian language pair by comparing an SMT system built from the crawled data with third-party systems. We finally present parallel datasets collected with our approach for the English-Croatian, English-Finnish, English-Serbian and English-Slovene language pairs.

This paper discusses the role that statistical machine translation (SMT) can play in the development of cross-border EU e-commerce,by highlighting extant obstacles and identifying relevant technologies to overcome them. In this sense, it firstly proposes a typology of e-commerce static and dynamic textual genres and it identifies those that may be more successfully targeted by SMT. The specific challenges concerning the automatic translation of user-generated content are discussed in detail. Secondly, the paper highlights the risk of data sparsity inherent to e-commerce and it explores the state-of-the-art strategies to achieve domain adequacy via adaptation. Thirdly, it proposes a robust workflow for the development of SMT systems adapted to the e-commerce domain by relying on inexpensive methods. Given the scarcity of user-generated language corpora for most language pairs, the paper proposes to obtain monolingual target-language data to train language models and aligned parallel corpora to tune and evaluate MT systems by means of crowdsourcing.

We propose the use of WordNet synsets in a syntax-based reordering model for hierarchical statistical machine translation (HPB-SMT) to enable the model to generalize to phrases not seen in the training data but that have equivalent meaning. We detail our methodology to incorporate synsets’ knowledge in the reordering model and evaluate the resulting WordNet-enhanced SMT systems on the English-to-Farsi language direction. The inclusion of synsets leads to the best BLEU score, outperforming the baseline (standard HPB-SMT) by 0.6 points absolute.

We acquire corpora from the domain of independent news from the Tlaxcala website. We build monolingual corpora for 15 languages and parallel corpora for all the combinations of those 15 languages. These corpora include languages for which only very limited such resources exist (e.g. Tamazight). We present the acquisition process in detail and we also present detailed statistics of the produced corpora, concerning mainly quantitative dimensions such as the size of the corpora per language (for the monolingual corpora) and per language pair (for the parallel corpora). To the best of our knowledge, these are the first publicly available parallel and monolingual corpora for the domain of independent news. We also create models for unsupervised sentence splitting for all the languages of the study.

This paper presents a novel approach for parallel data generation using machine translation and quality estimation. Our study focuses on pivot-based machine translation from English to Croatian through Slovene. We generate an English―Croatian version of the Europarl parallel corpus based on the English―Slovene Europarl corpus and the Apertium rule-based translation system for Slovene―Croatian. These experiments are to be considered as a first step towards the generation of reliable synthetic parallel data for under-resourced languages. We first collect small amounts of aligned parallel data for the Slovene―Croatian language pair in order to build a quality estimation system for sentence-level Translation Edit Rate (TER) estimation. We then infer TER scores on automatically translated Slovene to Croatian sentences and use the best translations to build an English―Croatian statistical MT system. We show significant improvement in terms of automatic metrics obtained on two test sets using our approach compared to a random selection of synthetic parallel data.

In this paper we present the construction process of a web corpus of Catalan built from the content of the .cat top-level domain. For collecting and processing data we use the Brno pipeline with the spiderling crawler and its accompanying tools. To the best of our knowledge the corpus represents the largest existing corpus of Catalan containing 687 million words, which is a significant increase given that until now the biggest corpus of Catalan, CuCWeb, counts 166 million words. We evaluate the resulting resource on the tasks of language modeling and statistical machine translation (SMT) by calculating LM perplexity and incorporating the LM in the SMT pipeline. We compare language models trained on different subsets of the resource with those trained on the Catalan Wikipedia and the target side of the parallel data used to train the SMT system.

This paper presents a study of user-perceived vs real machine translation (MT) post-editing effort and productivity gains, focusing on two bidirectional language pairs: English—German and English—Dutch. Twenty experienced media professionals post-edited statistical MT output and also manually translated comparative texts within a production environment. The paper compares the actual post-editing time against the users’ perception of the effort and time required to post-edit the MT output to achieve publishable quality, thus measuring real (vs perceived) productivity gains. Although for all the language pairs users perceived MT post-editing to be slower, in fact it proved to be a faster option than manual translation for two translation directions out of four, i.e. for Dutch to English, and (marginally) for English to German. For further objective scrutiny, the paper also checks the correlation of three state-of-the-art automatic MT evaluation metrics (BLEU, METEOR and TER) with the actual post-editing time.

This paper presents the platform developed in the PANACEA project, a distributed factory that automates the stages involved in the acquisition, production, updating and maintenance of Language Resources required by Machine Translation and other Language Technologies. We adopt a set of tools that have been successfully used in the Bioinformatics field, they are adapted to the needs of our field and used to deploy web services, which can be combined to build more complex processing chains (workflows). This paper describes the platform and its different components (web services, registry, workflows, social network and interoperability). We demonstrate the scalability of the platform by carrying out a set of massive data experiments. Finally, a validation of the platform across a set of required criteria proves its usability for different types of users (non-technical users and providers).

This paper proposes to enrich RBMT dictionaries with Named Entities (NEs) automatically acquired from Wikipedia. The method is applied to the Apertium English–Spanish system and its performance compared to that of Apertium with and without handtagged NEs. The system with automatic NEs outperforms the one without NEs, while results vary when compared to a system with handtagged NEs (results are comparable for Spanish→English but slightly worst for English→Spanish). Apart from that, adding automatic NEs contributes to decreasing the amount of unknown terms by more than 10%.

This paper presents an Italian→Catalan RBMT system automatically built by combining the linguistic data of the existing pairs Spanish–Catalan and Spanish–Italian. A lightweight manual postprocessing is carried out in order to fix inconsistencies in the automatically derived dictionaries and to add very frequent words that are missing according to a corpus analysis. The system is evaluated on the KDE4 corpus and outperforms Google Translate by approximately ten absolute points in terms of both TER and GTM.

We have adapted and extended the automatic Multilingual, Interoperable Named Entity Lexicon approach to Arabic, using Arabic WordNet (AWN) and Arabic Wikipedia (AWK). First, we extract AWN’s instantiable nouns and identify the corresponding categories and hyponym subcategories in AWK. Then, we exploit Wikipedia inter-lingual links to locate correspondences between articles in ten different languages in order to identify Named Entities (NEs). We apply keyword search on AWK abstracts to provide for Arabic articles that do not have a correspondence in any of the other languages. In addition, we perform a post-processing step to fetch further NEs from AWK not reachable through AWN. Finally, we investigate diacritization using matching with geonames databases, MADA-TOKAN tools and different heuristics for restoring vowel marks of Arabic NEs. Using this methodology, we have extracted approximately 45,000 Arabic NEs and built, to the best of our knowledge, the largest, most mature and well-structured Arabic NE lexical resource to date. We have stored and organised this lexicon following the LMF ISO standard. We conduct a quantitative and qualitative evaluation against a manually annotated gold standard and achieve precision scores from 95.83% (with 66.13% recall) to 99.31% (with 61.45% recall) according to different values of a threshold.

This paper presents the automatic extension of Princeton WordNet with Named Entities (NEs). This new resource is called Named Entity WordNet. Our method maps the noun is-a hierarchy of WordNet to Wikipedia categories, identifies the NEs present in the latter and extracts different information from them such as written variants, definitions, etc. This information is inserted into a NE repository. A module that converts from this generic repository to the WordNet specific format has been developed. The paper explores different aspects of our methodology such as the treatment of polysemous terms, the identification of hyponyms within the Wikipedia categorization system, the identification of Wikipedia articles which are NEs and the design of a NE repository compliant with the LMF ISO standard. So far, this procedure enriches WordNet with 310,742 NEs and 381,043 “instance of” relations.

EVALITA 2007, the first edition of the initiative devoted to the evaluation of Natural Language Processing tools for Italian, provided a shared framework where participants’ systems had the possibility to be evaluated on five different tasks, namely Part of Speech Tagging (organised by the University of Bologna), Parsing (organised by the University of Torino), Word Sense Disambiguation (organised by CNR-ILC, Pisa), Temporal Expression Recognition and Normalization (organised by CELCT, Trento), and Named Entity Recognition (organised by FBK, Trento). We believe that the diffusion of shared tasks and shared evaluation practices is a crucial step towards the development of resources and tools for Natural Language Processing. Experiences of this kind, in fact, are a valuable contribution to the validation of existing models and data, allowing for consistent comparisons among approaches and among representation schemes. The good response obtained by EVALITA, both in the number of participants and in the quality of results, showed that pursuing such goals is feasible not only for English, but also for other languages.

Notwithstanding its acknowledged richness, the SIMPLE semantic model does not offer the representational vocabulary for encoding some conceptual links holding between events and their participants and among co-participants in events. Although critical for boosting performance in many NLP application tasks, such deep lexical information is therefore only partially encoded in the SIMPLE-CLIPS Italian semantic database. This paper reports on the enrichment of the SIMPLE relation set by some expressive means, namely semantic relations, borrowed from the EuroWordNet model and their implementation in the SIMPLE-CLIPS lexicon. The original situation existing in the database, as to the expression of this type of information is described and the loan descriptive vocabulary presented. Strategies based on the exploitation of the source lexicon data were adopted to induce new information: a wide range of semantic - but also syntactic - information was investigated for singling out word senses candidate to be linked by the new relations. The lexicon enrichment by 5,000 new relations instantiated so far has therefore been carried out as a largely automated, low-effort and cost-free process, with no heavy human intervention. The redundancy set off by such an extension of information is being addressed by the implementation of inheritance in the SIMPLE-CLIPS database (Del Gratta et al., 2008).

This paper presents the application of inheritance to the formal taxonomy (is-a) of a semantically rich Language Resource based on the Generative Lexicon theory, SIMPLE-CLIPS. The aim is to lighten the representation of its semantic layer by reducing the number of encoded relations. A prediction calculation on the impact of introducing inheritance regarding space occupancy is carried out, yielding a significant space reduction of 22%. This is corroborated by its actual application, which reduces the number of explicitly encoded relations in this lexicon by 18.4%. Later on, we study the issues that inheritance poses to the Language Resources, and discuss sensitive solutions to tackle each of them, including examples. Finally, we present a discussion on the application of inheritance, from which two side effect advantages arise: consistency enhancement and inference capabilities.