ymoslem/acl-abstracts huggingface dataset:
Chat or Learn: a Data-Driven Robust Question-Answering System (2020)

We present a voice-based conversational agent which combines the robustness of chatbots and the utility of question answering (QA) systems. Indeed, while data-driven chatbots are typically user-friendly but not goal-oriented, QA systems tend to perform poorly at chitchat. The proposed chatbot relies on a controller which performs dialogue act classification and feeds user input either to a sequence-to-sequence chatbot or to a QA system. The resulting chatbot is a spoken QA application for the Google Home smart speaker. The system is endowed with general-domain knowledge from Wikipedia articles and uses coreference resolution to detect relatedness between questions. We present our choices of data sets for training and testing the components, and present the experimental results that helped us optimize the parameters of the chatbot. In particular, we discuss the appropriateness of using the SQuAD dataset for evaluating end-to-end QA, in the light of our system`s behavior.
Project PIAF: Building a Native French Question-Answering Dataset (2020)

Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.
Cross-lingual and Cross-domain Evaluation of Machine Reading Comprehension with Squad and CALOR-Quest Corpora (2020)

Machine Reading received recently a lot of attention thanks to both the availability of very large corpora such as SQuAD or MS MARCO containing triplets (document, question, answer), and the introduction of Transformer Language Models such as BERT which obtain excellent results, even matching human performance according to the SQuAD leaderboard. One of the key features of Transformer Models is their ability to be jointly trained across multiple languages, using a shared subword vocabulary, leading to the construction of cross-lingual lexical representations. This feature has been used recently to perform zero-shot cross-lingual experiments where a multilingual BERT model fine-tuned on a machine reading comprehension task exclusively for English was directly applied to Chinese and French documents with interesting performance. In this paper we study the cross-language and cross-domain capabilities of BERT on a Machine Reading Comprehension task on two corpora: SQuAD and a new French Machine Reading dataset, called CALOR-QUEST. The semantic annotation available on CALOR-QUEST allows us to give a detailed analysis on the kinds of questions that are properly handled through the cross-language process. We will try to answer this question: which factor between language mismatch and domain mismatch has the strongest influence on the performances of a Machine Reading Comprehension task?
ScholarlyRead: A New Dataset for Scientific Article Reading Comprehension (2020)

We present ScholarlyRead, span-of-word-based scholarly articles' Reading Comprehension (RC) dataset with approximately 10K manually checked passage-question-answer instances. ScholarlyRead was constructed in semi-automatic way. We consider the articles from two popular journals of a reputed publishing house. Firstly, we generate questions from these articles in an automatic way. Generated questions are then manually checked by the human annotators. We propose a baseline model based on Bi-Directional Attention Flow (BiDAF) network that yields the F1 score of 37.31{\%}. The framework would be useful for building Question-Answering (QA) systems on scientific articles.
Contextualized Embeddings based Transformer Encoder for Sentence Similarity Modeling in Answer Selection Task (2020)

Word embeddings that consider context have attracted great attention for various natural language processing tasks in recent years. In this paper, we utilize contextualized word embeddings with the transformer encoder for sentence similarity modeling in the answer selection task. We present two different approaches (feature-based and fine-tuning-based) for answer selection. In the feature-based approach, we utilize two types of contextualized embeddings, namely the Embeddings from Language Models (ELMo) and the Bidirectional Encoder Representations from Transformers (BERT) and integrate each of them with the transformer encoder. We find that integrating these contextual embeddings with the transformer encoder is effective to improve the performance of sentence similarity modeling. In the second approach, we fine-tune two pre-trained transformer encoder models for the answer selection task. Based on our experiments on six datasets, we find that the fine-tuning approach outperforms the feature-based approach on all of them. Among our fine-tuning-based models, the Robustly Optimized BERT Pretraining Approach (RoBERTa) model results in new state-of-the-art performance across five datasets.
Automatic Spanish Translation of SQuAD Dataset for Multi-lingual Question Answering (2020)

Recently, multilingual question answering became a crucial research topic, and it is receiving increased interest in the NLP community. However, the unavailability of large-scale datasets makes it challenging to train multilingual QA systems with performance comparable to the English ones. In this work, we develop the Translate Align Retrieve (TAR) method to automatically translate the Stanford Question Answering Dataset (SQuAD) v1.1 to Spanish. We then used this dataset to train Spanish QA systems by fine-tuning a Multilingual-BERT model. Finally, we evaluated our QA models with the recently proposed MLQA and XQuAD benchmarks for cross-lingual Extractive QA. Experimental results show that our models outperform the previous Multilingual-BERT baselines achieving the new state-of-the-art values of 68.1 F1 on the Spanish MLQA corpus and 77.6 F1 on the Spanish XQuAD corpus. The resulting, synthetically generated SQuAD-es v1.1 corpora, with almost 100{\%} of data contained in the original English version, to the best of our knowledge, is the first large-scale QA training resource for Spanish.
A Corpus for Visual Question Answering Annotated with Frame Semantic Information (2020)

Visual Question Answering (VQA) has been widely explored as a computer vision problem, however enhancing VQA systems with linguistic information is necessary for tackling the complexity of the task. The language understanding part can play a major role especially for questions asking about events or actions expressed via verbs. We hypothesize that if the question focuses on events described by verbs, then the model should be aware of or trained with verb semantics, as expressed via semantic role labels, argument types, and/or frame elements. Unfortunately, no VQA dataset exists that includes verb semantic information. We created a new VQA dataset annotated with verb semantic information called imSituVQA. imSituVQA is built by taking advantage of the imSitu dataset annotations. The imSitu dataset consists of images manually labeled with semantic frame elements, mostly taken from FrameNet.
Evaluation of Dataset Selection for Pre-Training and Fine-Tuning Transformer Language Models for Clinical Question Answering (2020)

We evaluate the performance of various Transformer language models, when pre-trained and fine-tuned on different combinations of open-domain, biomedical, and clinical corpora on two clinical question answering (QA) datasets (CliCR and emrQA). We perform our evaluations on the task of machine reading comprehension, which involves training the model to answer a question given an unstructured context paragraph. We conduct a total of 48 experiments on different combinations of the large open-domain and domain-specific corpora. We found that an initial fine-tuning on an open-domain dataset, SQuAD, consistently improves the clinical QA performance across all the model variants.
A Shared Task of a New, Collaborative Type to Foster Reproducibility: A First Exercise in the Area of Language Science and Technology with REPROLANG2020 (2020)

n this paper, we introduce a new type of shared task {---} which is collaborative rather than competitive {---} designed to support and fosterthe reproduction of research results. We also describe the first event running such a novel challenge, present the results obtained, discussthe lessons learned and ponder on future undertakings.
A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well (2020)

In this paper, we reproduce the experiments of Artetxe et al. (2018b) regarding the robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. We show that the reproduction of their method is indeed feasible with some minor assumptions. We further investigate the robustness of their model by introducing four new languages that are less similar to English than the ones proposed by the original paper. In order to assess the stability of their model, we also conduct a grid search over sensible hyperparameters. We then propose key recommendations that apply to any research project in order to deliver fully reproducible research.
A Closer Look on Unsupervised Cross-lingual Word Embeddings Mapping (2020)

In this work, we study the unsupervised cross-lingual word embeddings mapping method presented by Artetxe et al. (2018). First, wesuccessfully reproduced the experiments performed in the original work, finding only minor differences. Furthermore, we verified themethod`s robustness on different embedding representations and new language pairs, particularly these involving Slavic languages likePolish or Czech. We also performed an experimental analysis of the impact of the method`s parameters on the final result. Finally, welooked for an alternative way of initialization, which directly relies on the isometric assumption. Our work confirms the results presentedearlier, at the same time pointing at interesting problems occurring while using the method with different types of embeddings or onless-common language pairs.
Reproducing a Morphosyntactic Tagger with a Meta-BiLSTM Model over Context Sensitive Token Encodings (2020)

Reproducibility is generally regarded as being a requirement for any form of experimental science. Even so, reproduction of research results is only recently beginning to be practiced and acknowledged. In the context of the REPROLANG 2020 shared task, we contribute to this trend by reproducing the work reported on by Bohnet et al. (2018) on morphosyntactic tagging. Their meta-BiLSTM model achieved state-of-the-art results across a wide range of languages. This was done by integrating sentence-level and single-word context through synchronized training by a meta-model. Our reproduction only partially confirms the main results of the paper in terms of outperforming earlier models. The results of our reproductions improve on earlier models on the morphological tagging task, but not on the part-of-speech tagging task. Furthermore, even where we improve on earlier models, we fail to match the F1-scores reported for the meta-BiLSTM model. Because we chose not to contact the original authors for our reproduction study, the uncertainty about the degree of parallelism that was achieved between the original study and our reproduction limits the value of our findings as an assessment of the reliability of the original results. At the same time, however, it underscores the relevance of our reproduction effort in regard to the reproducibility and interpretability of those findings. The discrepancies between our findings and the original results demonstrate that there is room for improvement in many aspects of reporting regarding the reproducibility of the experiments. In addition, we suggest that different reporting choices could improve the interpretability of the results.
Reproducing Neural Ensemble Classifier for Semantic Relation Extraction inScientific Papers (2020)

Within the natural language processing (NLP) community, shared tasks play an important role. They define a common goal and allowthe the comparison of different methods on the same data. SemEval-2018 Task 7 involves the identification and classification of relationsin abstracts from computational linguistics (CL) publications. In this paper we describe an attempt to reproduce the methods and resultsfrom the top performing system at for SemEval-2018 Task 7. We describe challenges we encountered in the process, report on the resultsof our system, and discuss the ways that our attempt at reproduction can inform best practices.
ULMFiT replication (2020)

Authors: Mohamed Abdellatif and Ahmed Elgammal Gitlab URL: \url{https://gitlab.com/abdollatif/lrec_app} Commit hash: 3f20b2ddb96d8c865e5f56f5566edf371214785f Tag name: Splits2 Dataset file md5: 5aee3dac5e48d1ac3d279083212734c9 Dataset URL: \url{https://drive.google.com/file/d/1cv5HuQhgFVizupFI40dzreemS2gMM498/view?usp=sharing}
CombiNMT: An Exploration into Neural Text Simplification Models (2020)

This work presents a replication study of Exploring Neural Text Simplification Models (Nisioi et al., 2017). We were able to successfully replicate and extend the methods presented in the original paper. Alongside the replication results, we present our improvements dubbed CombiNMT. By using an updated implementation of OpenNMT, and incorporating the Newsela corpus alongside the original Wikipedia dataset (Hwang et al., 2016), as well as refining both datasets to select high quality training examples. Our work present two new systems, CombiNMT995, which is a result of matched sentences with a cosine similarity of 0.995 or less, and CombiNMT98, which, similarly, runs on a cosine similarity of 0.98 or less. By extending the human evaluation presented within the original paper, increasing both the number of annotators and the number of sentences annotated, with the intention of increasing the quality of the results, CombiNMT998 shows significant improvement over any of the Neural Text Simplification (NTS) systems from the original paper in terms of both the number of changes and the percentage of correct changes made.
Reproducing Monolingual, Multilingual and Cross-Lingual CEFR Predictions (2020)

his study aims to reproduce the research of Vajjala and Rama (2018) which showed that it is possible to predict the quality of a text written by learners of a given language by means of a model built on the basis of texts written by learners of another language. These authors also pointed out that POStag and dependency n-grams were significantly more effective than text length and global linguistic indices frequently used for this kind of task. The analyses performed show that some important points of their code did not correspond to the explanations given in the paper. These analyses confirm the possibility to use syntactic n-gram features in cross-lingual experiments to categorize texts according to their CEFR level (Common European Framework of Reference for Languages). However, text length and some classical indexes of readability are much more effective in the monolingual and the multilingual experiments than what Vajjala and Rama concluded and are even the best performing features when the cross-lingual task is seen as a regression problem. This study emphasized the importance for reproducibility of setting explicitly the reading order of the instances when using a K-fold CV procedure and, more generally, the need to properly randomize these instances before. It also evaluates a two-step procedure to determine the degree of statistical significance of the differences observed in a K-fold cross-validation schema and argues against the use of a Bonferroni-type correction in this context.
Reproduction and Replication: A Case Study with Automatic Essay Scoring (2020)

As in many experimental sciences, reproducibility of experiments has gained ever more attention in the NLP community. This paper presents our reproduction efforts of an earlier study of automatic essay scoring (AES) for determining the proficiency of second language learners in a multilingual setting. We present three sets of experiments with different objectives. First, as prescribed by the LREC 2020 REPROLANG shared task, we rerun the original AES system using the code published by the original authors on the same dataset. Second, we repeat the same experiments on the same data with a different implementation. And third, we test the original system on a different dataset and a different language. Most of our findings are in line with the findings of the original paper. Nevertheless, there are some discrepancies between our results and the results presented in the original paper. We report and discuss these differences in detail. We further go into some points related to confirmation of research findings through reproduction, including the choice of the dataset, reporting and accounting for variability, use of appropriate evaluation metrics, and making code and data available. We also discuss the varying uses and differences between the terms reproduction and replication, and we argue that reproduction, the confirmation of conclusions through independent experiments in varied settings is more valuable than exact replication of the published values.
REPROLANG 2020: Automatic Proficiency Scoring of Czech, English, German, Italian, and Spanish Learner Essays (2020)

We report on our attempts to reproduce the work described in Vajjala {\&} Rama 2018, {\textquoteleft}Experiments with universal CEFR classification', as part of REPROLANG 2020: this involves featured-based and neural approaches to essay scoring in Czech, German and Italian. Our results are broadly in line with those from the original paper, with some differences due to the stochastic nature of machine learning and programming language used. We correct an error in the reported metrics, introduce new baselines, apply the experiments to English and Spanish corpora, and generate adversarial data to test classifier robustness. We conclude that feature-based approaches perform better than neural network classifiers for text datasets of this size, though neural network modifications do bring performance closer to the best feature-based models.
Language Proficiency Scoring (2020)

The Common European Framework of Reference (CEFR) provides generic guidelines for the evaluation of language proficiency. Nevertheless, for automated proficiency classification systems, different approaches for different languages are proposed. Our paper evaluates and extends the results of an approach to Automatic Essay Scoring proposed as a part of the REPROLANG 2020 challenge. We provide a comparison between our results and the ones from the published paper and we include a new corpus for the English language for further experiments. Our results are lower than the expected ones when using the same approach and the system does not scale well with the added English corpus.
The Learnability of the Annotated Input in NMT Replicating (Vanmassenhove and Way, 2018) with OpenNMT (2020)

In this paper, we reproduce some of the experiments related to neural network training for Machine Translation as reported in (Vanmassenhove and Way, 2018). They annotated a sample from the EN-FR and EN-DE Europarl aligned corpora with syntactic and semantic annotations to train neural networks with the Nematus Neural Machine Translation (NMT) toolkit. Following the original publication, we obtained lower BLEU scores than the authors of the original paper, but on a more limited set of annotations. In the second half of the paper, we try to analyze the difference in the results obtained and suggest some methods to improve the results. We discuss the Byte Pair Encoding (BPE) used in the pre-processing phase and suggest feature ablation in relation to the granularity of syntactic and semantic annotations. The learnability of the annotated input is discussed in relation to existing resources for the target languages. We also discuss the feature representation likely to have been adopted for combining features.
KGvec2go -- Knowledge Graph Embeddings as a Service (2020)

In this paper, we present KGvec2go, a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications. Currently, we serve pre-trained embeddings for four knowledge graphs. We introduce the service and its usage, and we show further that the trained models have semantic value by evaluating them on multiple semantic benchmarks. The evaluation also reveals that the combination of multiple models can lead to a better outcome than the best individual model.
Ontology Matching Using Convolutional Neural Networks (2020)

In order to achieve interoperability of information in the context of the Semantic Web, it is necessary to find effective ways to align different ontologies. As the number of ontologies grows for a given domain, and as overlap between ontologies grows proportionally, it is becoming more and more crucial to develop accurate and reliable techniques to perform this task automatically. While traditional approaches to address this challenge are based on string metrics and structure analysis, in this paper we present a methodology to align ontologies automatically using machine learning techniques. Specifically, we use convolutional neural networks to perform string matching between class labels using character embeddings. We also rely on the set of superclasses to perform the best alignment. Our results show that we obtain state-of-the-art performance on ontologies from the Ontology Alignment Evaluation Initiative (OAEI). Our model also maintains good performance when tested on a different domain, which could lead to potential cross-domain applications.
Defying Wikidata: Validation of Terminological Relations in the Web of Data (2020)

In this paper we present an approach to validate terminological data retrieved from open encyclopaedic knowledge bases. This need arises from the enrichment of automatically extracted terms with information from existing resources in theLinguistic Linked Open Data cloud. Specifically, the resource employed for this enrichment is WIKIDATA, since it is one of the biggest knowledge bases freely available within the Semantic Web. During the experiment, we noticed that certain RDF properties in the Knowledge Base did not contain the data they are intended to represent, but a different type of information. In this paper we propose an approach to validate the retrieved data based on four axioms that rely on two linguistic theories: the x-bar theory and the multidimensional theory of terminology. The validation process is supported by a second knowledge base specialised in linguistic data; in this case, CONCEPTNET. In our experiment, we validate terms from the legal domain in four languages: Dutch, English, German and Spanish. The final aim is to generate a set of sound and reliable terminological resources in RDF to contribute to the population of the Linguistic Linked Open Data cloud.
Recent Developments for the Linguistic Linked Open Data Infrastructure (2020)

In this paper we describe the contributions made by the European H2020 project {\textquotedblleft}Pr{\^e}t-{\`a}-LLOD{\textquotedblright} ({\textquoteleft}Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors') to the further development of the Linguistic Linked Open Data (LLOD) infrastructure. Pr{\^e}t-{\`a}-LLOD aims to develop a new methodology for building data value chains applicable to a wide range of sectors and applications and based around language resources and language technologies that can be integrated by means of semantic technologies. We describe the methods implemented for increasing the number of language data sets in the LLOD. We also present the approach for ensuring interoperability and for porting LLOD data sets and services to other infrastructures, as well as the contribution of the projects to existing standards.