ymoslem/acl-abstracts huggingface dataset:
Un corpus en arabe annot\'e manuellement avec des sens WordNet (Arabic Manually Sense Annotated Corpus with WordNet Senses) (2018)

OntoNotes comprend le seul corpus manuellement annot{\'e} en sens librement disponible pour l`arabe. Elle reste peu connue et utilis{\'e}e certainement parce que le projet s`est achev{\'e} sans lier cet inventaire au Princeton WordNet qui lui aurait ouvert l`acc{\`e}s {\`a} son riche {\'e}cosyst{\`e}me. Dans cet article, nous pr{\'e}sentons une version {\'e}tendue de OntoNotes Release 5.0 que nous avons cr{\'e}{\'e}e en suivant une m{\'e}thodologie de construction semi-automatique. Il s`agit d`une mise {\`a} jour de la partie arabe annot{\'e}e en sens du corpus en ajoutant l`alignement vers le Princeton WordNet 3.0. Cette ressource qui comprend plus de 12 500 mots annot{\'e}s est librement disponible pour la communaut{\'e}. Nous esp{\'e}rons qu`elle deviendra un standard pour l'{\'e}valuation de la d{\'e}sambigu{\"i}sation lexicale de l`arabe.
DEFT2018 : recherche d`information et analyse de sentiments dans des tweets concernant les transports en \^Ile de France (DEFT2018 : Information Retrieval and Sentiment Analysis in Tweets about Public Transportation in \^Ile de France Region ) (2018)

Cet article pr{\'e}sente l'{\'e}dition 2018 de la campagne d'{\'e}valuation DEFT (D{\'e}fi Fouille de Textes). A partir d`un corpus de tweets, quatre t{\^a}ches ont {\'e}t{\'e} propos{\'e}es : identifier les tweets sur la th{\'e}matique des transports, puis parmi ces derniers, identifier la polarit{\'e} (n{\'e}gatif, neutre, positif, mixte), identifier les marqueurs de sentiment et la cible, et enfin, annoter compl{\`e}tement chaque tweet en source et cible des sentiments exprim{\'e}s. Douze {\'e}quipes ont particip{\'e}, majoritairement sur les deux premi{\`e}res t{\^a}ches. Sur l`identification de la th{\'e}matique des transports, la micro F-mesure varie de 0,827 {\`a} 0,908. Sur l`identification de la polarit{\'e} globale, la micro F-mesure varie de 0,381 {\`a} 0,823.
Participation d'EDF R\&D \`a DEFT 2018 (Here the title in English) (2018)

Ce papier d{\'e}crit la participation d`EDF R{\&}D {\`a} la campagne d'{\'e}valuation DEFT 2018. Notre {\'e}quipe a particip{\'e} aux deux premi{\`e}res t{\^a}ches : classification des tweets en transport/non-transport (T{\^a}che T1) et d{\'e}tection de la polarit{\'e} globale des tweets (T{\^a}che T2). Nous avons utilis{\'e} 3 m{\'e}thodes diff{\'e}rentes s`appuyant sur Word2Vec, CNN et LSTM. Aucune donn{\'e}e suppl{\'e}mentaire, autre que les donn{\'e}es d`apprentissage, n`a {\'e}t{\'e} utilis{\'e}e. Notre {\'e}quipe obtient des r{\'e}sultats tr{\`e}s corrects et se classe 1{\`e}re {\'e}quipe non acad{\'e}mique. Les m{\'e}thodes propos{\'e}es sont facilement transposables {\`a} d`autres t{\^a}ches de classification de textes courts et peuvent int{\'e}resser plusieurs entit{\'e}s du groupe EDF.
CLaC @ DEFT 2018: Sentiment analysis of tweets on transport from \^Ile-de-France (2018)

CLaC @ DEFT 2018: Analysis of tweets on transport on the {\^I}le-de-France This paper describes the system deployed by the CLaC lab at Concordia University in Montreal for the DEFT 2018 shared task. The competition consisted in four different tasks; however, due to lack of time, we only participated in the first two. We participated with a system based on conventional supervised learning methods: a support vector machine classifier and an artificial neural network. For task 1, our best approach achieved an F-measure of 87.61{\%}; while at task 2, we achieve 51.03{\%}, situating our system below the average of the other participants.
Mod\`eles en Caract\`eres pour la D\'etection de Polarit\'e dans les Tweets (Character-level Models for Polarity Detection in Tweets ) (2018)

Dans cet article, nous pr{\'e}sentons notre contribution au D{\'e}fi Fouille de Textes 2018 au travers de trois m{\'e}thodes originales pour la classification th{\'e}matique et la d{\'e}tection de polarit{\'e} dans des tweets en fran{\c{c}}ais. Nous y avons ajout{\'e} un syst{\`e}me de vote. Notre premi{\`e}re m{\'e}thode est fond{\'e}e sur des lexiques (mots et emojis), les n-grammes de caract{\`e}res et un classificateur {\`a} vaste marge (ou SVM). tandis que les deux autres sont des m{\'e}thodes endog{\`e}nes fond{\'e}es sur l`extraction de caract{\'e}ristiques au grain caract{\`e}res : un mod{\`e}le {\`a} m{\'e}moire {\`a} court-terme persistante (ou BiLSTM pour Bidirectionnal Long Short-Term Memory) et perceptron multi-couche d`une part et un mod{\`e}le de s{\'e}quences de caract{\`e}res ferm{\'e}es fr{\'e}quentes et classificateur SVM d`autre part. Le BiLSTM a produit de loin les meilleurs r{\'e}sultats puisqu`il a obtenu la premi{\`e}re place sur la t{\^a}che 1, classification binaire de tweets selon qu`ils traitent ou non des transports, et la troisi{\`e}me place sur la t{\^a}che 2, classification de la polarit{\'e} en 4 classes. Ce r{\'e}sultat est d`autant plus int{\'e}ressant que la m{\'e}thode propos{\'e}e est faiblement param{\'e}trique, totalement endog{\`e}ne et qu`elle n`implique aucun pr{\'e}-traitement.
Concat\'enation de r\'eseaux de neurones pour la classification de tweets, DEFT2018 (Concatenation of neural networks for tweets classification, DEFT2018 ) (2018)

Nous pr{\'e}sentons le syst{\`e}me utilis{\'e} par l'{\'e}quipe Melodi/Synapse D{\'e}veloppement dans la comp{\'e}tition DEFT2018 portant sur la classification de th{\'e}matique ou de sentiments de tweets en fran{\c{c}}ais. On propose un syst{\`e}me unique pour les deux approches qui combine concat{\'e}nativement deux m{\'e}thodes d`embedding et trois mod{\`e}les de repr{\'e}sentation s{\'e}quence. Le syst{\`e}me se classe 1/13 en analyse de sentiments et 4/13 en classification th{\'e}matique.
Participation de l'IRISA \`a DeFT 2018 : classification et annotation d`opinion dans des tweets (IRISA at DeFT 2018: classifying and tagging opinion in tweets ) (2018)

Cet article d{\'e}crit les syst{\`e}mes d{\'e}velopp{\'e}s par l'{\'e}quipe LinkMedia de l`IRISA pour la campagne d'{\'e}valuation DeFT 2018 portant sur l`analyse d`opinion dans des tweets en fran{\c{c}}ais. L'{\'e}quipe a particip{\'e} {\`a} 3 des 4 t{\^a}ches de la campagne : (i) classification des tweets selon s`ils concernent les transports ou non, (ii) classification des tweets selon leur polarit{\'e} et (iii) annotation des marqueurs d`opinion et de l`objet {\`a} propos duquel est exprim{\'e}e l`opinion. Nous avons utilis{\'e} un algorithme de boosting d`arbres de d{\'e}cision et des r{\'e}seaux de neurones r{\'e}currents (RNN) pour traiter les t{\^a}ches 1 et 2. Pour la t{\^a}che 3 nous avons exp{\'e}riment{\'e} l`utilisation de r{\'e}seaux de neurones r{\'e}currents associ{\'e}s {\`a} des CRF. Ces approches donnent des r{\'e}sultats proches, avec un l{\'e}ger avantage aux RNN, et ont permis d'{\^e}tre parmi les premiers class{\'e}s pour chacune des t{\^a}ches.
DEFT 2018: Attention s\'elective pour classification de microblogs (DEFT 2018 : Selective Attention for Microblogging Classification ) (2018)

Dans le cadre de l`atelier DEFT 2018 nous nous sommes int{\'e}ress{\'e}s {\`a} la classification de microblogs (ici, des tweets) r{\'e}dig{\'e}s en fran{\c{c}}ais. Ici, nous proposons une m{\'e}thode se basant sur un r{\'e}seau hi{\'e}rarchique de neurones r{\'e}current avec attention. La sp{\'e}cificit{\'e} de notre architecture est de prendre en compte {--}via un mechanisme d`attention et de portes{--} les hashtags et les mentions directes (e.g., @user), sp{\'e}cifiques aux microblogs. Notre mod{\`e}le a obtenu de tr{\`e}s bon r{\'e}sultats sur la premi{\`e}re t{\^a}che et des r{\'e}sultats comp{\'e}titifs sur la seconde.
Notre tweet premi\`ere fois au DEFT-2018 : syst\`emes de d\'etection de polarit\'e et de transports (Systems for detecting polarity and public transport discussions in French tweets) (2018)

Cet article d{\'e}crit les syst{\`e}mes de l'{\'e}quipe Eloquant pour la cat{\'e}gorisation de tweets en fran{\c{c}}ais dans les t{\^a}ches 1 (d{\'e}tection de la th{\'e}matique transports en commun) et 2 (d{\'e}tection de la polarit{\'e} globale) du DEFT 2018. Nos syst{\`e}mes reposent sur un enrichissement s{\'e}mantique, l`apprentissage automatique et, pour la t{\^a}che 1 une approche symbolique. Nous avons effectu{\'e} deux runs pour chacune des t{\^a}ches. Nos meilleures F-mesures (0.897 pour la t{\^a}che 1 et 0.800 pour la t{\^a}che 2) sont au-dessus de la moyenne globale pour chaque t{\^a}che, et nous placent dans les 30{\%} sup{\'e}rieurs de tous les runs pour la t{\^a}che 2.
LSE au DEFT 2018 : Classification de tweets bas\'ee sur les r\'eseaux de neurones profonds (LSE at DEFT 2018 : Sentiment analysis model based on deep learning) (2018)

Dans ce papier, nous d{\'e}crivons les syst{\`e}mes d{\'e}velopp{\'e}s au LSE pour le DEFT 2018 sur les t{\^a}ches 1 et 2 qui consistent {\`a} classifier des tweets. La premi{\`e}re t{\^a}che consiste {\`a} d{\'e}terminer si un message concerne les transports ou non. La deuxi{\`e}me, consiste {\`a} classifier les tweets selon leur polarit{\'e} globale. Pour les deux t{\^a}ches nous avons d{\'e}velopp{\'e} des syst{\`e}mes bas{\'e}s sur des r{\'e}seaux de neurones convolutifs (CNN) et r{\'e}currents (LSTM, BLSTM et GRU). Chaque mot d`un tweet donn{\'e} est repr{\'e}sent{\'e} par un vecteur dense appris {\`a} partir des donn{\'e}es relativement proches de celles de la comp{\'e}tition. Le score final officiel est de 0.891 pour la t{\^a}che 1 et de 0.781 pour la t{\^a}che 2.
Syllabs@DEFT2018 : combinaison de m\'ethodes de classification supervis\'ees (Syllabs@DEFT2018: Combination of Supervised Classification Methods) (2018)

Nous pr{\'e}sentons la participation de Syllabs {\`a} la t{\^a}che de classification de tweets dans le domaine du transport lors de DEFT 2018. Pour cette premi{\`e}re participation {\`a} une campagne DEFT, nous avons choisi de tester plusieurs algorithmes de classification {\'e}tat de l`art. Apr{\`e}s une {\'e}tape de pr{\'e}traitement commune {\`a} l`ensemble des algorithmes, nous effectuons un apprentissage sur le seul contenu des tweets. Les r{\'e}sultats {\'e}tant somme toute assez proches, nous effectuons un vote majoritaire sur les trois algorithmes ayant obtenus les meilleurs r{\'e}sultats.
LIRMM@DEFT-2018 -- Mod\`ele de classification de la vectorisation des documents (LIRMM DEFT-2018 -- Document Vectorization Classification model ) (2018)

Dans ce papier, nous d{\'e}crivons notre participation au d{\'e}fi d`analyse de texte DEFT 2018. Nous avons particip{\'e} {\`a} deux t{\^a}ches : (i) classification transport/non-transport et (ii) analyse de polarit{\'e} globale des tweets : positifs, negatifs, neutres et mixtes. Nous avons exploit{\'e} un r{\'e}seau de neurone bas{\'e} sur un perceptron multicouche mais utilisant une seule couche cach{\'e}e.
Adapted Sentiment Similarity Seed Words For French Tweets' Polarity Classification (2018)

We present, in this paper, our contribution in DEFT 2018 task 2 : {\textquotedblleft}Global polarity{\textquotedblright}, determining the overall polarity (Positive, Negative, Neutral or MixPosNeg) of tweets regarding public transport, in French language. Our system is based on a list of sentiment seed-words adapted for French public transport tweets. These seed-words are extracted from DEFT`s training annotated dataset, and the sentiment relations between seed-words and other terms are captured by cosine measure of their word embeddings representations, using a French language word embeddings model of 683k words. Our semi-supervised system achieved an F1-measure equals to 0.64.
The IWSLT 2018 Evaluation Campaign (2018)

The International Workshop of Spoken Language Translation (IWSLT) 2018 Evaluation Campaign featured two tasks: low-resource machine translation and speech translation. In the first task, manually transcribed speech had to be translated from Basque to English. Since this translation direction is a under-resourced language pair, participants were encouraged to use additional parallel data from related languages. In the second task, participants had to translate English audio into German text with a full speech-translation system. In the baseline condition, participants were free to use composite architectures, while in the end-to-end condition they were restricted to use a single model for the task. This year, eight research groups took part in the low-resource machine translation task and nine in the speech translation task.
Unsupervised Parallel Sentence Extraction from Comparable Corpora (2018)

Mining parallel sentences from comparable corpora is of great interest for many downstream tasks. In the BUCC 2017 shared task, systems performed well by training on gold standard parallel sentences. However, we often want to mine parallel sentences without bilingual supervision. We present a simple approach relying on bilingual word embeddings trained in an unsupervised fashion. We incorporate orthographic similarity in order to handle words with similar surface forms. In addition, we propose a dynamic threshold method to decide if a candidate sentence-pair is parallel which eliminates the need to fine tune a static value for different datasets. Since we do not employ any language specific engineering our approach is highly generic. We show that our approach is effective, on three language-pairs, without the use of any bilingual signal which is important because parallel sentence mining is most useful in low resource scenarios.
Word Rewarding for Adequate Neural Machine Translation (2018)

To improve the translation adequacy in neural machine translation (NMT), we propose a rewarding model with target word prediction using bilingual dictionaries inspired by the success of decoder constraints in statistical machine translation. In particular, the model first predicts a set of target words promising for translation; then boosts the probabilities of the predicted words to give them better chances to be output. Our rewarding model minimally interacts with the decoder so that it can be easily applied to the decoder of an existing NMT system. Extensive evaluation under both resource-rich and resource-poor settings shows that (1) BLEU score improves more than 10 points with oracle prediction, (2) BLEU score improves about 1.0 point with target word prediction using bilingual dictionaries created either manually or automatically, (3) hyper-parameters of our model are relatively easy to optimize, and (4) undergeneration problem can be alleviated in exchange for increasing over-generated words.
Analyzing Knowledge Distillation in Neural Machine Translation (2018)

Knowledge distillation has recently been successfully applied to neural machine translation. It allows for building shrunk networks while the resulting systems retain most of the quality of the original model. Despite the fact that many authors report on the benefits of knowledge distillation, few have discussed the actual reasons why it works, especially in the context of neural MT. In this paper, we conduct several experiments aimed at understanding why and how distillation impacts accuracy on an English-German translation task. We show that translation complexity is actually reduced when building a distilled/synthesised bi-text when compared to the reference bi-text. We further remove noisy data from synthesised translations and merge filtered synthesised data together with original reference, thus achieving additional gains in terms of accuracy.
Word-based Domain Adaptation for Neural Machine Translation (2018)

In this paper, we empirically investigate applying word-level weights to adapt neural machine translation to e-commerce domains, where small e-commerce datasets and large out-of-domain datasets are available. In order to mine in-domain like words in the out-of-domain datasets, we compute word weights by using a domain-specific and a non-domain-specific language model followed by smoothing and binary quantization. The baseline model is trained on mixed in-domain and out-of-domain datasets. Experimental results on En {\textrightarrow} Zh e-commerce domain translation show that compared to continuing training without word weights, it improves MT quality by up to 3.11{\%} BLEU absolute and 1.59{\%} TER. We have also trained models using fine-tuning on the in-domain data. Pre-training a model with word weights improves fine-tuning up to 1.24{\%} BLEU absolute and 1.64{\%} TER, respectively.
A Machine Translation Approach for Modernizing Historical Documents Using Backtranslation (2018)

Human language evolves with the passage of time. This makes historical documents to be hard to comprehend by contemporary people and, thus, limits their accessibility to scholars specialized in the time period in which a certain document was written. Modernization aims at breaking this language barrier and increase the accessibility of historical documents to a broader audience. To do so, it generates a new version of a historical document, written in the modern version of the document`s original language. In this work, we propose several machine translation approaches for modernizing historical documents. We tested these approaches in different scenarios, obtaining very encouraging results.
Multi-Source Neural Machine Translation with Data Augmentation (2018)

Multi-source translation systems translate from multiple languages to a single target language. By using information from these multiple sources, these systems achieve large gains in accuracy. To train these systems, it is necessary to have corpora with parallel text in multiple sources and the target language. However, these corpora are rarely complete in practice due to the difficulty of providing human translations in all of the relevant languages. In this paper, we propose a data augmentation approach to fill such incomplete parts using multi-source neural machine translation (NMT). In our experiments, results varied over different language combinations but significant gains were observed when using a source language similar to the target language.
Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary (2018)

We propose a method to transfer knowledge across neural machine translation (NMT) models by means of a shared dynamic vocabulary. Our approach allows to extend an initial model for a given language pair to cover new languages by adapting its vocabulary as long as new data become available (i.e., introducing new vocabulary items if they are not included in the initial model). The parameter transfer mechanism is evaluated in two scenarios: i) to adapt a trained single language NMT system to work with a new language pair and ii) to continuously add new language pairs to grow to a multilingual NMT system. In both the scenarios our goal is to improve the translation performance, while minimizing the training convergence time. Preliminary experiments spanning five languages with different training data sizes (i.e., 5k and 50k parallel sentences) show a significant performance gain ranging from +3.85 up to +13.63 BLEU in different language directions. Moreover, when compared with training an NMT model from scratch, our transfer-learning approach allows us to reach higher performance after training up to 4{\%} of the total training steps.
Machine Translation Human Evaluation: an investigation of evaluation based on Post-Editing and its relation with Direct Assessment (2018)

In this paper we present an analysis of the two most prominent methodologies used for the human evaluation of MT quality, namely evaluation based on Post-Editing (PE) and evaluation based on Direct Assessment (DA). To this purpose, we exploit a publicly available large dataset containing both types of evaluations. We first focus on PE and investigate how sensitive TER-based evaluation is to the type and number of references used. Then, we carry out a comparative analysis of PE and DA to investigate the extent to which the evaluation results obtained by methodologies addressing different human perspectives are similar. This comparison sheds light not only on PE but also on the so-called reference bias related to monolingual DA. Also, we analyze if and how the two methodologies can complement each other`s weaknesses.
The USTC-NEL Speech Translation system at IWSLT 2018 (2018)

This paper describes the USTC-NEL (short for {\textquotedblright}National Engineering Laboratory for Speech and Language Information Processing University of science and technology of china{\textquotedblright}) system to the speech translation task of the IWSLT Evaluation 2018. The system is a conventional pipeline system which contains 3 modules: speech recognition, post-processing and machine translation. We train a group of hybrid-HMM models for our speech recognition, and for machine translation we train transformer based neural machine translation models with speech recognition output style text as input. Experiments conducted on the IWSLT 2018 task indicate that, compared to baseline system from KIT, our system achieved 14.9 BLEU improvement.
The ADAPT System Description for the IWSLT 2018 Basque to English Translation Task (2018)

In this paper we present the ADAPT system built for the Basque to English Low Resource MT Evaluation Campaign. Basque is a low-resourced, morphologically-rich language. This poses a challenge for Neural Machine Translation models which usually achieve better performance when trained with large sets of data. Accordingly, we used synthetic data to improve the translation quality produced by a model built using only authentic data. Our proposal uses back-translated data to: (a) create new sentences, so the system can be trained with more data; and (b) translate sentences that are close to the test set, so the model can be fine-tuned to the document to be translated.
The University of Helsinki submissions to the IWSLT 2018 low-resource translation task (2018)

This paper presents the University of Helsinki submissions to the Basque{--}English low-resource translation task. Our primary system is a standard bilingual Transformer system, trained on the available parallel data and various types of synthetic data. We describe the creation of the synthetic datasets, some of which use a pivoting approach, in detail. One of our contrastive submissions is a multilingual model trained on comparable data, but without the synthesized parts. Our bilingual model with synthetic data performed best, obtaining 25.25 BLEU on the test data.
The MeMAD Submission to the IWSLT 2018 Speech Translation Task (2018)

This paper describes the MeMAD project entry to the IWSLT Speech Translation Shared Task, addressing the translation of English audio into German text. Between the pipeline and end-to-end model tracks, we participated only in the former, with three contrastive systems. We tried also the latter, but were not able to finish our end-to-end model in time. All of our systems start by transcribing the audio into text through an automatic speech recognition (ASR) model trained on the TED-LIUM English Speech Recognition Corpus (TED-LIUM). Afterwards, we feed the transcripts into English-German text-based neural machine translation (NMT) models. Our systems employ three different translation models trained on separate training sets compiled from the English-German part of the TED Speech Translation Corpus (TED-TRANS) and the OPENSUBTITLES2018 section of the OPUS collection. In this paper, we also describe the experiments leading up to our final systems. Our experiments indicate that using OPENSUBTITLES2018 in training significantly improves translation performance. We also experimented with various preand postprocessing routines for the NMT module, but we did not have much success with these. Our best-scoring system attains a BLEU score of 16.45 on the test set for this year`s task.