pwc-archive/papers-with-abstracts huggingface dataset:
Language Modeling with a General Second-Order RNN
Authors: Diego Maupom{\'e}, Marie-Jean Meurs
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Language Modeling, Language Modelling

Different Recurrent Neural Network (RNN) architectures update their state in different manners as the input sequence is processed. RNNs including a multiplicative interaction between their current state and the current input, second-order ones, show promising performance in language modeling. In this paper, we introduce a second-order RNNs that generalizes existing ones. Evaluating on the Penn Treebank dataset, we analyze how its different components affect its performance in character-lever recurrent language modeling. We perform our experiments controlling the parameter counts of models. We find that removing the first-order terms does not hinder performance. We perform further experiments comparing the effects of the relative size of the state space and the multiplicative interaction space on performance. Our expectation was that a larger states would benefit language models built on longer documents, and larger multiplicative interaction states would benefit ones built on larger input spaces. However, our results suggest that this is not the case and the optimal relative size is the same for both document tokenizations used.
Towards a Gold Standard for Evaluating Danish Word Embeddings
Authors: Nina Schneidermann, Rasmus Hvingelby, Bolette Pedersen
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Semantic Similarity, Semantic Textual Similarity, Word Embeddings

This paper presents the process of compiling a model-agnostic similarity goal standard for evaluating Danish word embeddings based on human judgments made by 42 native speakers of Danish. Word embeddings resemble semantic similarity solely by distribution (meaning that word vectors do not reflect relatedness as differing from similarity), and we argue that this generalization poses a problem in most intrinsic evaluation scenarios. In order to be able to evaluate on both dimensions, our human-generated dataset is therefore designed to reflect the distinction between relatedness and similarity. The goal standard is applied for evaluating the {``}goodness{''} of six existing word embedding models for Danish, and it is discussed how a relatively low correlation can be explained by the fact that semantic similarity is substantially more challenging to model than relatedness, and that there seems to be a need for future human judgments to measure similarity in full context and along more than a single spectrum.
Urban Dictionary Embeddings for Slang NLP Applications
Authors: Steven Wilson, Walid Magdy, Barbara McGillivray, Kiran Garimella, Gareth Tyson
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Clustering, Sarcasm Detection, Semantic Similarity, Semantic Textual Similarity, Sentiment Analysis, Word Embeddings

The choice of the corpus on which word embeddings are trained can have a sizable effect on the learned representations, the types of analyses that can be performed with them, and their utility as features for machine learning models. To contribute to the existing sets of pre-trained word embeddings, we introduce and release the first set of word embeddings trained on the content of Urban Dictionary, a crowd-sourced dictionary for slang words and phrases. We show that although these embeddings are trained on fewer total tokens (by at least an order of magnitude compared to most popular pre-trained embeddings), they have high performance across a range of common word embedding evaluations, ranging from semantic similarity to word clustering tasks. Further, for some extrinsic tasks such as sentiment analysis and sarcasm detection where we expect to require some knowledge of colloquial language on social media data, initializing classifiers with the Urban Dictionary Embeddings resulted in improved performance compared to initializing with a range of other well-known, pre-trained embeddings that are order of magnitude larger in size.
Representation Learning for Unseen Words by Bridging Subwords to Semantic Networks
Authors: Yeachan Kim, Kang-Min Kim, SangKeun Lee
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Representation Learning, Word Embeddings

Pre-trained word embeddings are widely used in various fields. However, the coverage of pre-trained word embeddings only includes words that appeared in corpora where pre-trained embeddings are learned. It means that the words which do not appear in training corpus are ignored in tasks, and it could lead to the limited performance of neural models. In this paper, we propose a simple yet effective method to represent out-of-vocabulary (OOV) words. Unlike prior works that solely utilize subword information or knowledge, our method makes use of both information to represent OOV words. To this end, we propose two stages of representation learning. In the first stage, we learn subword embeddings from the pre-trained word embeddings by using an additive composition function of subwords. In the second stage, we map the learned subwords into semantic networks (e.g., WordNet). We then re-train the subword embeddings by using lexical entries on semantic lexicons that could include newly observed subwords. This two-stage learning makes the coverage of words broaden to a great extent. The experimental results clearly show that our method provides consistent performance improvements over strong baselines that use subwords or lexical resources separately.
On the Correlation of Word Embedding Evaluation Metrics
Authors: Fran{\c{c}}ois Torregrossa, Vincent Claveau, Nihel Kooli, Guillaume Gravier, Robin Allesiardo
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: All, Word Embeddings

Word embeddings intervene in a wide range of natural language processing tasks. These geometrical representations are easy to manipulate for automatic systems. Therefore, they quickly invaded all areas of language processing. While they surpass all predecessors, it is still not straightforward why and how they do so. In this article, we propose to investigate all kind of evaluation metrics on various datasets in order to discover how they correlate with each other. Those correlations lead to 1) a fast solution to select the best word embeddings among many others, 2) a new criterion that may improve the current state of static Euclidean word embeddings, and 3) a way to create a set of complementary datasets, i.e. each dataset quantifies a different aspect of word embeddings.
CBOW-tag: a Modified CBOW Algorithm for Generating Embedding Models from Annotated Corpora
Authors: Attila Nov{\'a}k, L{\'a}szl{\'o} Laki, Borb{\'a}la Nov{\'a}k
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: TAG

In this paper, we present a modified version of the CBOW algorithm implemented in the fastText framework. Our modified algorithm, CBOW-tag builds a vector space model that includes the representation of the original word forms and their annotation at the same time. We illustrate the results by presenting a model built from a corpus that includes morphological and syntactic annotations. The simultaneous presence of unannotated elements and different annotations at the same time in the model makes it possible to constrain nearest neighbour queries to specific types of elements. The model can thus efficiently answer questions such as What do we eat?, What can we do with a skeleton? What else do we do with what we eat?, etc. Error analysis reveals that the model can highlight errors introduced into the annotation by the tagger and parser we used to generate the annotations as well as lexical peculiarities in the corpus itself, especially if we do not limit the vocabulary of the model to frequent items.
Much Ado About Nothing -- Identification of Zero Copulas in Hungarian Using an NMT Model
Authors: Andrea D{\"o}m{\"o}t{\"o}r, Zijian Gy{\H{o}}z{\H{o}} Yang, Attila Nov{\'a}k
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: NMT

The research presented in this paper concerns zero copulas in Hungarian, i.e. the phenomenon that nominal predicates lack an explicit verbal copula in the default present tense 3rd person indicative case. We created a tool based on the state-of-the-art transformer architecture implemented in Marian NMT framework that can identify and mark the location of zero copulas, i.e. the position where an overt copula would appear in the non-default cases. Our primary aim was to support quantitative corpus-based linguistic research by creating a tool that can be used to compile a corpus of significant size containing examples of nominal predicates including the location of the zero copulas. We created the training corpus for our system transforming sentences containing overt copulas into ones containing zero copula labels. However, we first needed to disambiguate occurrences of the massively ambiguous verb van {`}exist/be/have{'}. We performed this using a rule-base classifier relying on English translations in the English-Hungarian parallel subcorpus of the OpenSubtitles corpus. We created several NMT-based models using different sampling methods and optionally using our baseline model to synthesize additional training data. Our best model obtains almost 90{\%} precision and 80{\%} recall on an in-domain test set.
Improving NMT Quality Using Terminology Injection
Authors: Duane K. Dougal, Deryle Lonsdale
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Machine Translation, NMT, Translation

Many organizations use domain- or organization-specific words and phrases. This paper explores the use of vetted terminology as an input to neural machine translation (NMT) for improved results: ensuring that the translation of individual terms is consistent with an approved multilingual terminology collection. We discuss, implement, and evaluate a method for injecting terminology and for evaluating terminology injection. Our use of the long short-term memory (LSTM) attention mechanism prevalent in state-of-the-art NMT systems involves attention vectors for correctly identifying semantic entities and aligning the tokens that represent them, both in the source and the target languages. Appropriate terminology is then injected into matching alignments during decoding. We also introduce a new translation metric more sensitive to approved terminological content in MT output.
Word Embedding Evaluation in Downstream Tasks and Semantic Analogies
Authors: Joaquim Santos, Bernardo Consoli, Renata Vieira
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Semantic Similarity, Semantic Textual Similarity, Word Embeddings

Language Models have long been a prolific area of study in the field of Natural Language Processing (NLP). One of the newer kinds of language models, and some of the most used, are Word Embeddings (WE). WE are vector space representations of a vocabulary learned by a non-supervised neural network based on the context in which words appear. WE have been widely used in downstream tasks in many areas of study in NLP. These areas usually use these vector models as a feature in the processing of textual data. This paper presents the evaluation of newly released WE models for the Portuguese langauage, trained with a corpus composed of 4.9 billion tokens. The first evaluation presented an intrinsic task in which WEs had to correctly build semantic and syntactic relations. The second evaluation presented an extrinsic task in which the WE models were used in two downstream tasks: Named Entity Recognition and Semantic Similarity between Sentences. Our results show that a diverse and comprehensive corpus can often outperform a larger, less textually diverse corpus, and that batch training may cause quality loss in WE models.
Detection of Reading Absorption in User-Generated Book Reviews: Resources Creation and Evaluation
Authors: Piroska Lendvai, S{\'a}ndor Dar{\'a}nyi, Christian Geng, Moniek Kuijpers, Oier Lopez de Lacalle, Jean-Christophe Mensonides, Simone Rebora, Uwe Reichel
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Binary Classification, Sentence, Sentence Embedding, Sentence-Embedding

To detect how and when readers are experiencing engagement with a literary work, we bring together empirical literary studies and language technology via focusing on the affective state of absorption. The goal of our resource development is to enable the detection of different levels of reading absorption in millions of user-generated reviews hosted on social reading platforms. We present a corpus of social book reviews in English that we annotated with reading absorption categories. Based on these data, we performed supervised, sentence level, binary classification of the explicit presence vs. absence of the mental state of absorption. We compared the performances of classical machine learners where features comprised sentence representations obtained from a pretrained embedding model (Universal Sentence Encoder) vs. neural classifiers in which sentence embedding vector representations are adapted or fine-tuned while training for the absorption recognition task. We discuss the challenges in creating the labeled data as well as the possibilities for releasing a benchmark corpus.
Developing an Arabic Infectious Disease Ontology to Include Non-Standard Terminology
Authors: Lama Alsudias, Paul Rayson
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles, Term Extraction

Building ontologies is a crucial part of the semantic web endeavour. In recent years, research interest has grown rapidly in supporting languages such as Arabic in NLP in general but there has been very little research on medical ontologies for Arabic. We present a new Arabic ontology in the infectious disease domain to support various important applications including the monitoring of infectious disease spread via social media. This ontology meaningfully integrates the scientific vocabularies of infectious diseases with their informal equivalents. We use ontology learning strategies with manual checking to build the ontology. We applied three statistical methods for term extraction from selected Arabic infectious diseases articles: TF-IDF, C-value, and YAKE. We also conducted a study, by consulting around 100 individuals, to discover the informal terms related to infectious diseases in Arabic. In future work, we will automatically extract the relations for infectious disease concepts but for now these are manually created. We report two complementary experiments to evaluate the ontology. First, a quantitative evaluation of the term extraction results and an additional qualitative evaluation by a domain expert.
Aligning Wikipedia with WordNet:a Review and Evaluation of Different Techniques
Authors: Antoni Oliver
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles

In this paper we explore techniques for aligning Wikipedia articles with WordNet synsets, their successful alignment being our main goal. We evaluate techniques that use the definitions and sense relations in Wordnet and the text and categories in Wikipedia articles. The results we present are based on two evaluation strategies: one uses a new gold and silver standard (for which the creation process is explained); the other creates wordnets in other languages and then compares them with existing wordnets for those languages found in the Open Multilingual Wordnet project. A reliable alignment between WordNet and Wikipedia is a very valuable resource for the creation of new wordnets in other languages and for the development of existing wordnets. The evaluation of alignments between WordNet and lexical resources is a difficult and time-consuming task, but the evaluation strategy using the Open Multilingual Wordnet can be used as an automated evaluation measure to assess the quality of alignments between these two resources.
The MWN.PT WordNet for Portuguese: Projection, Validation, Cross-lingual Alignment and Distribution
Authors: Ant{\'o}nio Branco, Sara Grilo, M{\'a}rcia Bolrinha, Chakaveh Saedi, Ruben Branco, Jo{\~a}o Silva, Andreia Querido, Rita de Carvalho, Rosa Gaudio, Mariana Avel{\~a}s, Clara Pinto
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The objective of the present paper is twofold, to present the MWN.PT WordNet and to report on its construction and on the lessons learned with it. The MWN.PT WordNet for Portuguese includes 41,000 concepts, expressed by 38,000 lexical units. Its synsets were manually validated and are linked to semantically equivalent synsets of the Princeton WordNet of English, and thus transitively to the many wordnets for other languages that are also linked to this English wordnet. To the best of our knowledge, it is the largest high quality, manually validated and cross-lingually integrated, wordnet of Portuguese distributed for reuse. Its construction was initiated more than one decade ago and its description is published for the first time in the present paper. It follows a three step {\textless}projection, validation with alignment, completion{\textgreater} methodology consisting on the manual validation and expansion of the outcome of an automatic projection procedure of synsets and their hypernym relations, followed by another automatic procedure that transferred the relations of remaining semantic types across wordnets of different languages.
Ontology-Style Relation Annotation: A Case Study
Authors: Savong Bou, Naoki Suzuki, Makoto Miwa, Yutaka Sasaki
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Relation, Relation Extraction

This paper proposes an Ontology-Style Relation (OSR) annotation approach. In conventional Relation Extraction (RE) datasets, relations are annotated as links between entity mentions. In contrast, in our OSR annotation, a relation is annotated as a relation mention (i.e., not a link but a node) and domain and range links are annotated from the relation mention to its argument entity mentions. We expect the following benefits: (1) the relation annotations can be easily converted to Resource Description Framework (RDF) triples to populate an Ontology, (2) some part of conventional RE tasks can be tackled as Named Entity Recognition (NER) tasks. The relation classes are limited to several RDF properties such as domain, range, and subClassOf, and (3) OSR annotations can be clear documentations of Ontology contents. As a case study, we converted an in-house corpus of Japanese traffic rules in conventional annotations into the OSR annotations and built a novel OSR-RoR (Rules of the Road) corpus. The inter-annotator agreements of the conversion were 85-87{\%}. We evaluated the performance of neural NER and RE tools on the conventional and OSR annotations. The experimental results showed that the OSR annotations make the RE task easier while introducing slight complexity into the NER task.
The Ontology of Bulgarian Dialects -- Architecture and Information Retrieval
Authors: Rositsa Dekova
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Diagnostic, Information Retrieval, Retrieval

Following a concise description of the structure, the paper focuses on the potential of the Ontology of the Bulgarian Dialects, which demonstrates a novel usage of the ontological modelling for the purposes of dialect digital archiving and information processing. The ontology incorporates information on the dialects of the Bulgarian language and includes data from 84 dialects, spoken not only on the territory of the Republic of Bulgaria, but also abroad. It encodes both their geographical distribution and some of their main diagnostic features, such as the different mutations (also referred to as reflexes) of some of the Old Bulgarian vowels. The mutations modelled so far in the ontology include the reflex of the back nasal vowel /ѫ/ under stress, the reflex of the back er vowel /ъ/ under stress, and the reflex of the yat vowel /ѣ/ under stress when it precedes a syllable with a back vowel. Besides the opportunity for formal structuring of the considerable amount of data gathered through the years by dialectologists, the ontology also provides numerous possibilities for information retrieval {--} searches by dialect, country, dialect region, city or village, various combinations of diagnostic features.
Spatial AMR: Expanded Spatial Annotation in the Context of a Grounded Minecraft Corpus
Authors: Julia Bonn, Martha Palmer, Zheng Cai, Kristin Wright-Bettner
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Abstract Meaning Representation, Minecraft, Sentence

This paper presents an expansion to the Abstract Meaning Representation (AMR) annotation schema that captures fine-grained semantically and pragmatically derived spatial information in grounded corpora. We describe a new lexical category conceptualization and set of spatial annotation tools built in the context of a multimodal corpus consisting of 170 3D structure-building dialogues between a human architect and human builder in Minecraft. Minecraft provides a particularly beneficial spatial relation-elicitation environment because it automatically tracks locations and orientations of objects and avatars in the space according to an absolute Cartesian coordinate system. Through a two-step process of sentence-level and document-level annotation designed to capture implicit information, we leverage these coordinates and bearings in the AMRs in combination with spatial framework annotation to ground the spatial language in the dialogues to absolute space.
English WordNet Random Walk Pseudo-Corpora
Authors: Filip Klubi{\v{c}}ka, Alfredo Maldonado, Abhijit Mahalunkar, John Kelleher
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Word Embeddings

This is a resource description paper that describes the creation and properties of a set of pseudo-corpora generated artificially from a random walk over the English WordNet taxonomy. Our WordNet taxonomic random walk implementation allows the exploration of different random walk hyperparameters and the generation of a variety of different pseudo-corpora. We find that different combinations of parameters result in varying statistical properties of the generated pseudo-corpora. We have published a total of 81 pseudo-corpora that we have used in our previous research, but have not exhausted all possible combinations of hyperparameters, which is why we have also published a codebase that allows the generation of additional WordNet taxonomic pseudo-corpora as needed. Ultimately, such pseudo-corpora can be used to train taxonomic word embeddings, as a way of transferring taxonomic knowledge into a word embedding space.
On the Formal Standardization of Terminology Resources: The Case Study of TriMED
Authors: Federica Vezzani, Giorgio Maria Di Nunzio
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Management

The process of standardization plays an important role in the management of terminological resources. In this context, we present the work of re-modeling an existing multilingual terminological database for the medical domain, named TriMED. This resource was conceived in order to tackle some problems related to the complexity of medical terminology and to respond to different users{'} needs. We provide a methodology that should be followed in order to make a termbase compliant to the three most recent ISO/TC 37 standards. In particular, we focus on the definition of i) the structural meta-model of the resource, ii) the data categories provided, and iii) the TBX format for its implementation. In addition to the formal standardization of the resource, we describe the realization of a new data category repository for the management of the TriMED terminological data and a Web application that can be used to access the multilingual terminological records.