ymoslem/acl-abstracts huggingface dataset:
Comparative Study of Sentence Embeddings for Contextual Paraphrasing (2020)

Paraphrasing is an important aspect of natural-language generation that can produce more variety in the way specific content is presented. Traditionally, paraphrasing has been focused on finding different words that convey the same meaning. However, in human-human interaction, we regularly express our intention with phrases that are vastly different regarding both word content and syntactic structure. Instead of exchanging only individual words, the complete surface realisation of a sentences is altered while still preserving its meaning and function in a conversation. This kind of contextual paraphrasing did not yet receive a lot of attention from the scientific community despite its potential for the creation of more varied dialogues. In this work, we evaluate several existing approaches to sentence encoding with regard to their ability to capture such context-dependent paraphrasing. To this end, we define a paraphrase classification task that incorporates contextual paraphrases, perform dialogue act clustering, and determine the performance of the sentence embeddings in a sentence swapping task.
HypoNLI: Exploring the Artificial Patterns of Hypothesis-only Bias in Natural Language Inference (2020)

Many recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by merely looking at the hypothesis while completely ignoring the premise. In this work, we manage to derive adversarial examples in terms of the hypothesis-only bias and explore eligible ways to mitigate such bias. Specifically, we extract various phrases from the hypotheses (artificial patterns) in the training sets, and show that they have been strong indicators to the specific labels. We then figure out {\textquoteleft}hard' and {\textquoteleft}easy' instances from the original test sets whose labels are opposite to or consistent with those indications. We also set up baselines including both pretrained models (BERT, RoBerta, XLNet) and competitive non-pretrained models (InferSent, DAM, ESIM). Apart from the benchmark and baselines, we also investigate two debiasing approaches which exploit the artificial pattern modeling to mitigate such hypothesis-only bias: down-sampling and adversarial training. We believe those methods can be treated as competitive baselines in NLI debiasing tasks.
SAPPHIRE: Simple Aligner for Phrasal Paraphrase with Hierarchical Representation (2020)

We present SAPPHIRE, a Simple Aligner for Phrasal Paraphrase with HIerarchical REpresentation. Monolingual phrase alignment is a fundamental problem in natural language understanding and also a crucial technique in various applications such as natural language inference and semantic textual similarity assessment. Previous methods for monolingual phrase alignment are language-resource intensive; they require large-scale synonym/paraphrase lexica and high-quality parsers. Different from them, SAPPHIRE depends only on a monolingual corpus to train word embeddings. Therefore, it is easily transferable to specific domains and different languages. Specifically, SAPPHIRE first obtains word alignments using pre-trained word embeddings and then expands them to phrase alignments by bilingual phrase extraction methods. To estimate the likelihood of phrase alignments, SAPPHIRE uses phrase embeddings that are hierarchically composed of word embeddings. Finally, SAPPHIRE searches for a set of consistent phrase alignments on a lattice of phrase alignment candidates. It achieves search-efficiency by constraining the lattice so that all the paths go through a phrase alignment pair with the highest alignment score. Experimental results using the standard dataset for phrase alignment evaluation show that SAPPHIRE outperforms the previous method and establishes the state-of-the-art performance.
TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages (2020)

This paper presents TaPaCo, a freely available paraphrase corpus for 73 languages extracted from the Tatoeba database. Tatoeba is a crowdsourcing project mainly geared towards language learners. Its aim is to provide example sentences and translations for particular linguistic constructions and words. The paraphrase corpus is created by populating a graph with Tatoeba sentences and equivalence links between sentences {\textquotedblleft}meaning the same thing{\textquotedblright}. This graph is then traversed to extract sets of paraphrases. Several language-independent filters and pruning steps are applied to remove uninteresting sentences. A manual evaluation performed on three languages shows that between half and three quarters of inferred paraphrases are correct and that most remaining ones are either correct but trivial, or near-paraphrases that neutralize a morphological distinction. The corpus contains a total of 1.9 million sentences, with 200 - 250 000 sentences per language. It covers a range of languages for which, to our knowledge, no other paraphrase dataset exists. The dataset is available at \url{https://doi.org/10.5281/zenodo.3707949}.
Automated Fact-Checking of Claims from Wikipedia (2020)

Automated fact checking is becoming increasingly vital as both truthful and fallacious information accumulate online. Research on fact checking has benefited from large-scale datasets such as FEVER and SNLI. However, such datasets suffer from limited applicability due to the synthetic nature of claims and/or evidence written by annotators that differ from real claims and evidence on the internet. To this end, we present WikiFactCheck-English, a dataset of 124k+ triples consisting of a claim, context and an evidence document extracted from English Wikipedia articles and citations, as well as 34k+ manually written claims that are refuted by the evidence documents. This is the largest fact checking dataset consisting of real claims and evidence to date; it will allow the development of fact checking systems that can better process claims and evidence in the real world. We also show that for the NLI subtask, a logistic regression system trained using existing and novel features achieves peak accuracy of 68{\%}, providing a competitive baseline for future work. Also, a decomposable attention model trained on SNLI significantly underperforms the models trained on this dataset, suggesting that models trained on manually generated data may not be sufficiently generalizable or suitable for fact checking real-world claims.
Towards the Necessity for Debiasing Natural Language Inference Datasets (2020)

Modeling natural language inference is a challenging task. With large annotated data sets available it has now become feasible to train complex neural network based inference methods which achieve state of the art performance. However, it has been shown that these models also learn from the subtle biases inherent in these datasets (CITATION). In this work we explore two techniques for delexicalization that modify the datasets in such a way that we can control the importance that neural-network based methods place on lexical entities. We demonstrate that the proposed methods not only maintain the performance in-domain but also improve performance in some out-of-domain settings. For example, when using the delexicalized version of the FEVER dataset, the in-domain performance of a state of the art neural network method dropped only by 1.12{\%} while its out-of-domain performance on the FNC dataset improved by 4.63{\%}. We release the delexicalized versions of three common datasets used in natural language inference. These datasets are delexicalized using two methods: one which replaces the lexical entities in an overlap-aware manner, and a second, which additionally incorporates semantic lifting of nouns and verbs to their WordNet hypernym synsets
A French Corpus for Semantic Similarity (2020)

Semantic similarity is an area of Natural Language Processing that is useful for several downstream applications, such as machine translation, natural language generation, information retrieval, or question answering. The task consists in assessing the extent to which two sentences express or do not express the same meaning. To do so, corpora with graded pairs of sentences are required. The grade is positioned on a given scale, usually going from 0 (completely unrelated) to 5 (equivalent semantics). In this work, we introduce such a corpus for French, the first that we know of. It is comprised of 1,010 sentence pairs with grades from five annotators. We describe the annotation process, analyse these data, and perform a few experiments for the automatic grading of semantic similarity.
Developing Dataset of Japanese Slot Filling Quizzes Designed for Evaluation of Machine Reading Comprehension (2020)

This paper describes our developing dataset of Japanese slot filling quizzes designed for evaluation of machine reading comprehension. The dataset consists of quizzes automatically generated from Aozora Bunko, and each quiz is defined as a 4-tuple: a context passage, a query holding a slot, an answer character and a set of possible answer characters. The query is generated from the original sentence, which appears immediately after the context passage on the target book, by replacing the answer character into the slot. The set of possible answer characters consists of the answer character and the other characters who appear in the context passage. Because the context passage and the query shares the same context, a machine which precisely understand the context may select the correct answer from the set of possible answer characters. The unique point of our approach is that we focus on characters of target books as slots to generate queries from original sentences, because they play important roles in narrative texts and precise understanding their relationship is necessary for reading comprehension. To extract characters from target books, manually created dictionaries of characters are employed because some characters appear as common nouns not as named entities.
Detecting Negation Cues and Scopes in Spanish (2020)

In this work we address the processing of negation in Spanish. We first present a machine learning system that processes negation in Spanish. Specifically, we focus on two tasks: i) negation cue detection and ii) scope identification. The corpus used in the experimental framework is the SFU Corpus. The results for cue detection outperform state-of-the-art results, whereas for scope detection this is the first system that performs the task for Spanish. Moreover, we provide a qualitative error analysis aimed at understanding the limitations of the system and showing which negation cues and scopes are straightforward to predict automatically, and which ones are challenging.
TIARA: A Tool for Annotating Discourse Relations and Sentence Reordering (2020)

This paper introduces TIARA, a new publicly available web-based annotation tool for discourse relations and sentence reordering. Annotation tasks such as these, which are based on relations between large textual objects, are inherently hard to visualise without either cluttering the display and/or confusing the annotators. TIARA deals with the visual complexity during the annotation process by systematically simplifying the layout, and by offering interactive visualisation, including coloured links, indentation, and dual-view. TIARA`s text view allows annotators to focus on the analysis of logical sequencing between sentences. A separate tree view allows them to review their analysis in terms of the overall discourse structure. The dual-view gives it an edge over other discourse annotation tools and makes it particularly attractive as an educational tool (e.g., for teaching students how to argue more effectively). As it is based on standard web technologies and can be easily customised to other annotation schemes, it can be easily used by anybody. Apart from the project it was originally designed for, in which hundreds of texts were annotated by three annotators, TIARA has already been adopted by a second discourse annotation study, which uses it in the teaching of argumentation.
Infrastructure for Semantic Annotation in the Genomics Domain (2020)

We describe a novel super-infrastructure for biomedical text mining which incorporates an end-to-end pipeline for the collection, annotation, storage, retrieval and analysis of biomedical and life sciences literature, combining NLP and corpus linguistics methods. The infrastructure permits extreme-scale research on the open access PubMed Central archive. It combines an updatable Gene Ontology Semantic Tagger (GOST) for entity identification and semantic markup in the literature, with a NLP pipeline scheduler (Buster) to collect and process the corpus, and a bespoke columnar corpus database (LexiDB) for indexing. The corpus database is distributed to permit fast indexing, and provides a simple web front-end with corpus linguistics methods for sub-corpus comparison and retrieval. GOST is also connected as a service in the Language Application (LAPPS) Grid, in which context it is interoperable with other NLP tools and data in the Grid and can be combined with them in more complex workflows. In a literature based discovery setting, we have created an annotated corpus of 9,776 papers with 5,481,543 words.
Correcting the Autocorrect: Context-Aware Typographical Error Correction via Training Data Augmentation (2020)

In this paper, we explore the artificial generation of typographical errors based on real-world statistics. We first draw on a small set of annotated data to compute spelling error statistics. These are then invoked to introduce errors into substantially larger corpora. The generation methodology allows us to generate particularly challenging errors that require context-aware error detection. We use it to create a set of English language error detection and correction datasets. Finally, we examine the effectiveness of machine learning models for detecting and correcting errors based on this data.
KidSpell: A Child-Oriented, Rule-Based, Phonetic Spellchecker (2020)

For help with their spelling errors, children often turn to spellcheckers integrated in software applications like word processors and search engines. However, existing spellcheckers are usually tuned to the needs of traditional users (i.e., adults) and generally prove unsatisfactory for children. Motivated by this issue, we introduce KidSpell, an English spellchecker oriented to the spelling needs of children. KidSpell applies (i) an encoding strategy for mapping both misspelled words and spelling suggestions to their phonetic keys and (ii) a selection process that prioritizes candidate spelling suggestions that closely align with the misspelled word based on their respective keys. To assess the effectiveness of, we compare the model`s performance against several popular, mainstream spellcheckers in a number of offline experiments using existing and novel datasets. The results of these experiments show that KidSpell outperforms existing spellcheckers, as it accurately prioritizes relevant spelling corrections when handling misspellings generated by children in both essay writing and online search tasks. As a byproduct of our study, we create two new datasets comprised of spelling errors generated by children from hand-written essays and web search inquiries, which we make available to the research community.
ThaiLMCut: Unsupervised Pretraining for Thai Word Segmentation (2020)

We propose ThaiLMCut, a semi-supervised approach for Thai word segmentation which utilizes a bi-directional character language model (LM) as a way to leverage useful linguistic knowledge from unlabeled data. After the language model is trained on substantial unlabeled corpora, the weights of its embedding and recurrent layers are transferred to a supervised word segmentation model which continues fine-tuning them on a word segmentation task. Our experimental results demonstrate that applying the LM always leads to a performance gain, especially when the amount of labeled data is small. In such cases, the F1 Score increased by up to 2.02{\%}. Even on abig labeled dataset, a small improvement gain can still be obtained. The approach has also shown to be very beneficial for out-of-domain settings with a gain in F1 Score of up to 3.13{\%}. Finally, we show that ThaiLMCut can outperform other open source state-of-the-art models achieving an F1 Score of 98.78{\%} on the standard benchmark, InterBEST2009.
CCOHA: Clean Corpus of Historical American English (2020)

Modelling language change is an increasingly important area of interest within the fields of sociolinguistics and historical linguistics. In recent years, there has been a growing number of publications whose main concern is studying changes that have occurred within the past centuries. The Corpus of Historical American English (COHA) is one of the most commonly used large corpora in diachronic studies in English. This paper describes methods applied to the downloadable version of the COHA corpus in order to overcome its main limitations, such as inconsistent lemmas and malformed tokens, without compromising its qualitative and distributional properties. The resulting corpus CCOHA contains a larger number of cleaned word tokens which can offer better insights into language change and allow for a larger variety of tasks to be performed.
Outbound Translation User Interface Ptakop\vet: A Pilot Study (2020)

It is not uncommon for Internet users to have to produce a text in a foreign language they have very little knowledge of and are unable to verify the translation quality. We call the task {\textquotedblleft}outbound translation{\textquotedblright} and explore it by introducing an open-source modular system Ptakop{\v{e}}t. Its main purpose is to inspect human interaction with MT systems enhanced with additional subsystems, such as backward translation and quality estimation. We follow up with an experiment on (Czech) human annotators tasked to produce questions in a language they do not speak (German), with the help of Ptakop{\v{e}}t. We focus on three real-world use cases (communication with IT support, describing administrative issues and asking encyclopedic questions) from which we gain insight into different strategies users take when faced with outbound translation tasks. Round trip translation is known to be unreliable for evaluating MT systems but our experimental evaluation documents that it works very well for users, at least on MT systems of mid-range quality.
Seshat: a Tool for Managing and Verifying Annotation Campaigns of Audio Data (2020)

We introduce Seshat, a new, simple and open-source software to efficiently manage annotations of speech corpora. The Seshat software allows users to easily customise and manage annotations of large audio corpora while ensuring compliance with the formatting and naming conventions of the annotated output files. In addition, it includes procedures for checking the content of annotations following specific rules that can be implemented in personalised parsers. Finally, we propose a double-annotation mode, for which Seshat computes automatically an associated inter-annotator agreement with the gamma measure taking into account the categorisation and segmentation discrepancies.
Dragonfly: Advances in Non-Speaker Annotation for Low Resource Languages (2020)

Dragonfly is an open source software tool that supports annotation of text in a low resource language by non-speakers of the language. Using semantic and contextual information, non-speakers of a language familiar with the Latin script can produce high quality named entity annotations to support construction of a name tagger. We describe a procedure for annotating low resource languages using Dragonfly that others can use, which we developed based on our experience annotating data in more than ten languages. We also present performance comparisons between models trained on native speaker and non-speaker annotations.
Natural Language Processing Pipeline to Annotate Bulgarian Legislative Documents (2020)

The paper presents the Bulgarian MARCELL corpus, part of a recently developed multilingual corpus representing the national legislation in seven European countries and the NLP pipeline that turns the web crawled data into structured, linguistically annotated dataset. The Bulgarian data is web crawled, extracted from the original HTML format, filtered by document type, tokenised, sentence split, tagged and lemmatised with a fine-grained version of the Bulgarian Language Processing Chain, dependency parsed with NLP- Cube, annotated with named entities (persons, locations, organisations and others), noun phrases, IATE terms and EuroVoc descriptors. An orchestrator process has been developed to control the NLP pipeline performing an end-to-end data processing and annotation starting from the documents identification and ending in the generation of statistical reports. The Bulgarian MARCELL corpus consists of 25,283 documents (at the beginning of November 2019), which are classified into eleven types.
CLDFBench: Give Your Cross-Linguistic Data a Lift (2020)

While the amount of cross-linguistic data is constantly increasing, most datasets produced today and in the past cannot be considered FAIR (findable, accessible, interoperable, and reproducible). To remedy this and to increase the comparability of cross-linguistic resources, it is not enough to set up standards and best practices for data to be collected in the future. We also need consistent workflows for the {\textquotedblleft}retro-standardization{\textquotedblright} of data that has been published during the past decades and centuries. With the Cross-Linguistic Data Formats initiative, first standards for cross-linguistic data have been presented and successfully tested. So far, however, CLDF creation was hampered by the fact that it required a considerable degree of computational proficiency. With cldfbench, we introduce a framework for the retro-standardization of legacy data and the curation of new datasets that drastically simplifies the creation of CLDF by providing a consistent, reproducible workflow that rigorously supports version control and long term archiving of research data and code. The framework is distributed in form of a Python package along with usage information and examples for best practice. This study introduces the new framework and illustrates how it can be applied by showing how a resource containing structural and lexical data for Sinitic languages can be efficiently retro-standardized and analyzed.
KonText: Advanced and Flexible Corpus Query Interface (2020)

We present an advanced, highly customizable corpus query interface KonText built on top of core libraries of the open-source corpus search engine NoSketch Engine (NoSkE). The aim is to overcome some limitations of the original NoSkE user interface and provide integration capabilities allowing connection of the basic search service with other language resources (LRs). The introduced features are based on long-term feedback given by the users and researchers of the Czech National Corpus (CNC) along with other LRs providers running KonText as a part of their services. KonText is a fully operational and mature software deployed at the CNC since 2014 that currently handles thousands user queries per day.
Word at a Glance: Modular Word Profile Aggregator (2020)

Word at a Glance (WaG) is a word profile aggregator that provides means for exploring individual words, their comparison and translation, based on existing language resources and related software services. It is designed as a building kit-like application that fetches data from different sources and compiles them into a single, comprehensible and structured web page. WaG can be easily configured to support many tasks, but in general, it is intended to be used not only by language experts but also the general public.