ymoslem/acl-abstracts huggingface dataset:
Towards the Automatic Identification of Light Verb Constructions in Bulgarian (2016)

This paper presents work in progress focused on developing a method for automatic identification of light verb constructions (LVCs) as a subclass of Bulgarian verbal MWEs. The method is based on machine learning and is trained on a set of LVCs extracted from the Bulgarian WordNet (BulNet) and the Bulgarian National Corpus (BulNC). The machine learning uses lexical, morphosyntactic, syntactic and semantic features of LVCs. We trained and tested two separate classifiers using the Java package Weka and two learning decision tree algorithms {--} J48 and RandomTree. The evaluation of the method includes 10-fold cross-validation on the training data from BulNet (F1 = 0.766 obtained by the J48 decision tree algorithm and F1 = 0.725 by the RandomTree algorithm), as well as evaluation of the performance on new instances from the BulNC (F1 = 0.802 by J48 and F1 = 0.607 by the RandomTree algorithm). Preliminary filtering of the candidates gives a slight improvement (F1 = 0.802 by J48 and F1 = 0.737 by RandomTree).
HR4EU -- Using Language Resources in Computer Aided Language Learning (2016)

In this paper we present the HR4EU {--} web portal for e-learning of Croatian language. The web portal offers a new method of computer aided language learning (CALL) by encouraging language learners to use different language resources available for Croatian: corpora, inflectional and derivational morphological lexicons, treebank, Wordnet, etc. Apart from the previously developed language resources, the new ones are created in order to further facilitate the learning of Croatian language. We will focus on the usage of the treebank annotated at syntactic and semantic level in the CALL and describe the new HR4EU sub-corpus of the Croatian Dependency Treebank (HOBS). The HR4EU sub-corpus consists of approx. 550 sentences, which are manually annotated on syntactic and semantic role level according to the specifications used for the HOBS. The syntactic and the semantic structure of the sentence can be visualized as a dependency tree via the SynSem Visualizer. The visualization of the syntactic and the semantic structure of sentences will help users to produce syntactically and semantically correct sentences on their own.
SynTags -- Web Interface for Syntactic and Semantic Annotation (2016)

This paper presents a web tool for syntactic and semantic annotation and two of its applications. It gives the linguists the possibility to work with corpora and syntactic and semantic frames in XML format without having computer skills. The system is OS and platform independent and could be used both online and offline.
Finding Good Answers in Online Forums: Community Question Answering for Bulgarian (2016)

Community Question Answering (CQA) is a form of question answering that is getting increasingly popular as a research direction recently. Given a question posted in an online community forum and the thread of answers to it, a common formulation of the task is to rank automatically the answers, so that the good ones are ranked higher than the bad ones. Despite the vast research in CQA for English, very little attention has been paid to other languages. To bridge this gap, here we present our method for Community Question Answering in Bulgarian. We create annotated training and testing datasets for Bulgarian, and we further explore the applicability of machine translation for reusing English CQA data for building a Bulgarian system. The evaluation results show improvement over the baseline and can serve as a basis for further research.
Quotation Retrieval System for Bulgarian Media Content (2016)

This paper presents a method for automatic retrieval and attribution of quotations from media texts in Bulgarian. It involves recognition of report verbs (including their analytical forms) and syntactic patterns introducing quotations, as well as source attribution of the quote by identification of personal names, descriptors, and anaphora. The method is implemented in a fully-functional online system which offers a live service processing media content and extracting quotations on a daily basis. The system collects and processes written news texts from six Bulgarian media websites. The results are presented in a structured way with description, as well as sorting and filtering functionalities which facilitate the monitoring and analysis of media content. The method has been applied to extract quotations from English texts as well and can be adapted to work with other languages, provided that the respective language specific resources are supplied.
Stress Patterns of Compounds and MWEs in English and Bulgarian (2016)

The paper presents an ongoing research on the stress patterns of compounds and MWEs of the type ADJ+N and their corresponding free NPs in English and Bulgarian. The research focuses on the identification and the formal representation of the possible stress patterns of compounds and MWEs and free NPs. During our research so far, we have compiled a corpus of over 2000 compounds and MWEs, approx. 1000 for each language {--} English and Bulgarian. Our theoretical framework includes elements from different theories, i.e. the Generative Phonology Theory, the Metrical Theory, and the Theory of Primary accent first which all define the stress as a prosodic element. Our main goals are to specify the prosodic region where the stress is defined in English and Bulgarian MWEs and noun phrases and to define the main features of the stress in MWEs and free NPs in English and Bulgarian. The results of our research can serve for implementation into NLP modules for spoken language processing and generation.
Verbal Multiword Expressions in Croatian (2016)

The paper deals with verbal multiword expressions in Croatian. We focus on four types of verbal constructions: light verb constructions, i.e. constructions consisting of a light verb and a noun or prepositional phrase, complex predicate constructions, i.e. constructions consisting of a finite and infinitive verb, prepositional verb constructions, i.e. constructions consisting of a verb and a typical preposition, and, finally, verbal idioms, i.e. constructions with completely idiosyncratic meanings. All the constructions are annotated in the Universal Dependency treebank for Croatian. The identification of verbal multiword expressions is an important task in numerous NLP tasks. It is also important to define and delimitate this concept in linguistic theory.
A Simple Approach to Unifying Ambiguously Encoded Kurdish Characters (2016)

In this study we outline a potential problem in the normalisation stage of processing texts that are based on a modified version of the Arabic alphabet. The main source of resources available for processing resource-scarce languages is raw text. We have identified an interesting challenge that must be addressed when normalising certain natural language texts. Many less-resourced languages, such as Kurdish, Farsi, Urdu, Pashtu, etc., use a modified version of the Arabic writing system. Many characters in harvested data from the Internet may have exactly the same form but encoded with different Unicode values (ambiguous characters). It is important to identify ambiguous characters during the normalisation stage of most text processing tasks. We will demonstrate cases related to ambiguous Kurdish and Farsi characters and propose a semi-automatic approach to identifying and unifying ambiguously encoded characters.
A Possible Solution to the Problem of Machine Translation of Verb Forms from Bulgarian to English (2016)

The paper{\textquoteleft}s main subject is concerned with the problems related to machine translation of verb forms from Bulgarian to English. In separate sections of this article we discuss the problems related to differences between word formation in both languages and differences in the information that the verb forms grammaticalize. We also introduce the idea of implementing the statistical method of machine translation altogether with the rule-based method as a proposal for future research and the possible practical and theoretical outcomes.
Instance Selection for Online Automatic Post-Editing in a multi-domain scenario (2016)

In recent years, several end-to-end online translation systems have been proposed to successfully incorporate human post-editing feedback in the translation workflow. The performance of these systems in a multi-domain translation environment (involving different text genres, post-editing styles, machine translation systems) within the automatic post-editing (APE) task has not been thoroughly investigated yet. In this work, we show that when used in the APE framework the existing online systems are not robust towards domain changes in the incoming data stream. In particular, these systems lack in the capability to learn and use domain-specific post-editing rules from a pool of multi-domain data sets. To cope with this problem, we propose an online learning framework that generates more reliable translations with significantly better quality as compared with the existing online and batch systems. Our framework includes: i) an instance selection technique based on information retrieval that helps to build domain-specific APE systems, and ii) an optimization procedure to tune the feature weights of the log-linear model that allows the decoder to improve the post-editing quality.
Machine Translation Quality and Post-Editor Productivity (2016)

We assessed how different machine translation (MT) systems affect the post-editing (PE) process and product of professional English{--}Spanish translators. Our model found that for each 1-point increase in BLEU, there is a PE time decrease of 0.16 seconds per word, about 3-4{\%}. The MT system with the lowest BLEU score produced the output that was post-edited to the lowest quality and with the highest PE effort, measured both in HTER and actual PE operations.
Fuzzy-match repair using black-box machine translation systems: what can be expected? (2016)

Computer-aided translation (CAT) tools often use a translation memory (TM) as the key resource to assist translators. A TM contains translation units (TU) which are made up of source and target language segments; translators use the target segments in the TU suggested by the CAT tool by converting them into the desired translation. Proposals from TMs could be made more useful by using techniques such as fuzzy-match repair (FMR) which modify words in the target segment corresponding to mismatches identified in the source segment. Modifications in the target segment are done by translating the mismatched source sub-segments using an external source of bilingual information (SBI) and applying the translations to the corresponding positions in the target segment. Several combinations of translated sub-segments can be applied to the target segment which can produce multiple repair candidates. We provide a formal algorithmic description of a method that is capable of using any SBI to generate all possible fuzzy-match repairs and perform an oracle evaluation on three different language pairs to ascertain the potential of the method to improve translation productivity. Using DGT-TM translation memories and the machine system Apertium as the single source to build repair operators in three different language pairs, we show that the best repaired fuzzy matches are consistently closer to reference translations than either machine-translated segments or unrepaired fuzzy matches.
Fast, Scalable Phrase-Based SMT Decoding (2016)

The utilization of statistical machine translation (SMT) has grown enormously over the last decade, many using open-source software developed by the NLP community. As commercial use has increased, there is need for software that is optimized for commercial requirements, in particular, fast phrase-based decoding and more efficient utilization of modern multicore servers. In this paper we re-examine the major components of phrase-based decoding and decoder implementation with particular emphasis on speed and scalability on multicore machines. The result is a drop-in replacement for the Moses decoder which is up to fifteen times faster and scales monotonically with the number of cores.
Ranking suggestions for black-box interactive translation prediction systems with multilayer perceptrons (2016)

The objective of interactive translation prediction (ITP), a paradigm of computer-aided translation, is to assist professional translators by offering context-based computer-generated suggestions as they type. While most state-of-the-art ITP systems are tightly coupled to a machine translation (MT) system (often created ad-hoc for this purpose), our proposal follows a resourceagnostic approach, one that does not need access to the inner workings of the bilingual resources (MT systems or any other bilingual resources) used to generate the suggestions, thus allowing to include new resources almost seamlessly. As we do not expect the user to tolerate more than a few proposals each time, the set of potential suggestions need to be filtered and ranked; the resource-agnostic approach has been evaluated before using a set of intuitive length-based and position-based heuristics designed to determine which suggestions to show, achieving promising results. In this paper, we propose a more principled suggestion ranking approach using a regressor (a multilayer perceptron) that achieves significantly better results.
Multi-domain Adaptation for Statistical Machine Translation Based on Feature Augmentation (2016)

Domain adaptation is a major challenge when applying machine translation to practical tasks. In this paper, we present domain adaptation methods for machine translation that assume multiple domains. The proposed methods combine two model types: a corpus-concatenated model covering multiple domains and single-domain models that are accurate but sparse in specific domains. We combine the advantages of both models using feature augmentation for domain adaptation in machine learning. Our experimental results show that the BLEU scores of the proposed method clearly surpass those of single-domain models for low-resource domains. For high-resource domains, the scores of the proposed method were superior to those of both single-domain and corpusconcatenated models. Even in domains having a million bilingual sentences, the translation quality was at least preserved and even improved in some domains. These results demonstrate that state-of-the-art domain adaptation can be realized with appropriate settings, even when using standard log-linear models.
Bilingual Methods for Adaptive Training Data Selection for Machine Translation (2016)

In this paper, we propose a new data selection method which uses semi-supervised convolutional neural networks based on bitokens (Bi-SSCNNs) for training machine translation systems from a large bilingual corpus. In earlier work, we devised a data selection method based on semi-supervised convolutional neural networks (SSCNNs). The new method, Bi-SSCNN, is based on bitokens, which use bilingual information. When the new methods are tested on two translation tasks (Chinese-to-English and Arabic-to-English), they significantly outperform the other three data selection methods in the experiments. We also show that the BiSSCNN method is much more effective than other methods in preventing noisy sentence pairs from being chosen for training. More interestingly, this method only needs a tiny amount of in-domain data to train the selection model, which makes fine-grained topic-dependent translation adaptation possible. In the follow-up experiments, we find that neural machine translation (NMT) is more sensitive to noisy data than statistical machine translation (SMT). Therefore, Bi-SSCNN which can effectively screen out noisy sentence pairs, can benefit NMT much more than SMT.We observed a BLEU improvement over 3 points on an English-to-French WMT task when Bi-SSCNNs were used.
Neural Interactive Translation Prediction (2016)

We present an interactive translation prediction method based on neural machine translation. Even with the same translation quality of the underlying machine translation systems, the neural prediction method yields much higher word prediction accuracy (61.6{\%} vs. 43.3{\%}) than the traditional method based on search graphs, mainly due to better recovery from errors. We also develop efficient means to enable practical deployment.
Guided Alignment Training for Topic-Aware Neural Machine Translation (2016)

In this paper, we propose an effective way for biasing the attention mechanism of a sequence-to-sequence neural machine translation (NMT) model towards the well-studied statistical word alignment models. We show that our novel guided alignment training approach improves translation quality on real-life e-commerce texts consisting of product titles and descriptions, overcoming the problems posed by many unknown words and a large type/token ratio. We also show that meta-data associated with input texts such as topic or category information can significantly improve translation quality when used as an additional signal to the decoder part of the network. With both novel features, the BLEU score of the NMT system on a product title set improves from 18.6 to 21.3{\%}. Even larger MT quality gains are obtained through domain adaptation of a general domain NMT system to e-commerce data. The developed NMT system also performs well on the IWSLT speech translation task, where an ensemble of four variant systems outperforms the phrase-based baseline by 2.1{\%} BLEU absolute.
Improving Neural Machine Translation on resource-limited pairs using auxiliary data of a third language (2016)

In the recent years interest in Deep Neural Networks (DNN) has grown in the field of Natural Language Processing, as new training methods have been proposed. The usage of DNN has achieved state-of-the-art performance in various areas. Neural Machine Translation (NMT) described by Bahdanau et al. (2014) and its successive variations have shown promising results. DNN, however, tend to over-fit on small data-sets, which makes this method impracticable for resource-limited language pairs. This article combines three different ideas (splitting words into smaller units, using an extra dataset of a related language pair and using monolingual data) for improving the performance of NMT models on language pairs with limited data. Our experiments show that, in some cases, our proposed approach to subword-units performs better than BPE (Byte pair encoding) and that auxiliary language-pairs and monolingual data can help improve the performance of languages with limited resources.
Which Words Matter in Defining Phrase Reordering Behavior in Statistical Machine Translation? (2016)

Lexicalized and hierarchical reordering models use relative frequencies of fully lexicalized phrase pairs to learn phrase reordering distributions. This results in unreliable estimation for infrequent phrase pairs which also tend to be longer phrases. There are some smoothing techniques used to smooth the distributions in these models. But these techniques are unable to address the similarities between phrase pairs and their reordering distributions. We propose two models to use shorter sub-phrase pairs of an original phrase pair to smooth the phrase reordering distributions. In the first model we follow the classic idea of backing off to shorter histories commonly used in language model smoothing. In the second model, we use syntactic dependencies to identify the most relevant words in a phrase to back off to. We show how these models can be easily applied to existing lexicalized and hierarchical reordering models. Our models achieve improvements of up to 0.40 BLEU points in Chinese-English translation compared to a baseline which uses a regular lexicalized reordering model and a hierarchical reordering model. The results show that not all the words inside a phrase pair are equally important in defining phrase reordering behavior and shortening towards important words will decrease the sparsity problem for long phrase pairs.
Automatic Construction of Morphologically Motivated Translation Models for Highly Inflected, Low-Resource Languages (2016)

Statistical Machine Translation (SMT) of highly inflected, low-resource languages suffers from the problem of low bitext availability, which is exacerbated by large inflectional paradigms. When translating into English, rich source inflections have a high chance of being poorly estimated or out-of-vocabulary (OOV). We present a source language-agnostic system for automatically constructing phrase pairs from foreign-language inflections and their morphological analyses using manually constructed datasets, including Wiktionary. We then demonstrate the utility of these phrase tables in improving translation into English from Finnish, Czech, and Turkish in simulated low-resource settings, finding substantial gains in translation quality. We report up to +2.58 BLEU in a simulated low-resource setting and +1.65 BLEU in a moderateresource setting. We release our morphologically-motivated translation models, with tens of thousands of inflections in each of 8 languages.
Investigating the Impact of Various Partial Diacritization Schemes on Arabic-English Statistical Machine Translation (2016)

Most diacritics in Arabic represent short vowels. In Arabic orthography, such diacritics are considered optional. The absence of these diacritics naturally leads to significant word ambiguity to top the inherent ambiguity present in fully diacritized words. Word ambiguity is a significant impediment for machine translation. Despite the ambiguity presented by lack of diacritization, context helps ameliorate the situation. Identifying the appropriate amount of diacritic restoration to reduce word sense ambiguity in the context of machine translation is the object of this paper. Diacritic marks help reduce the number of possible lexical word choices assigned to a source word which leads to better quality translated sentences. We investigate a variety of (linguistically motivated) partial diacritization schemes that preserve some of the semantics that in essence complement the implicit contextual information present in the sentences. We also study the effect of training data size and report results on three standard test sets that represent a combination of different genres. The results show statistically significant improvements for some schemes compared to two baselines: text with no diacritics (the typical writing system adopted for Arabic) and text that is fully diacritized.