Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective Crowdsourcing of Multiple Tasks for Comprehensive Knowledge Extraction

Information extraction from unstructured texts plays a vital role in the field of natural language processing. Although there has been extensive research into each information extraction task (i.e., entity linking, coreference resolution, and relation extraction), data are not available for a continuous and coherent evaluation of all information extraction tasks in a comprehensive framework. Given that each task is performed and evaluated with a different dataset, analyzing the effect of the previous task on the next task with a single dataset throughout the information extraction process is impossible. This paper aims to propose a Korean information extraction initiative point and promote research in this field by presenting crowdsourcing data collected for four information extraction tasks from the same corpus and the training and evaluation results for each task of a state-of-the-art model. These machine learning data for Korean information extraction are the first of their kind, and there are plans to continuously increase the data volume. The test results will serve as an initiative result for each Korean information extraction task and are expected to serve as a comparison target for various studies on Korean information extraction using the data collected in this study.
SMASH Corpus: A Spontaneous Speech Corpus Recording Third-person Audio Commentaries on Gameplay

Developing a spontaneous speech corpus would be beneficial for spoken language processing and understanding. We present a speech corpus named the SMASH corpus, which includes spontaneous speech of two Japanese male commentators that made third-person audio commentaries during the gameplay of a fighting game. Each commentator ad-libbed while watching the gameplay with various topics covering not only explanations of each moment to convey the information on the fight but also comments to entertain listeners. We made transcriptions and topic tags as annotations on the recorded commentaries with our two-step method. We first made automatic and manual transcriptions of the commentaries and then manually annotated the topic tags. This paper describes how we constructed the SMASH corpus and reports some results of the annotations.
An HMM Approach with Inherent Model Selection for Sign Language and Gesture Recognition

HMMs have been the one of the first models to be applied for sign recognition and have become the baseline models due to their success in modeling sequential and multivariate data. Despite the extensive use of HMMs for sign recognition, determining the HMM structure has still remained as a challenge, especially when the number of signs to be modeled is high. In this work, we present a continuous HMM framework for modeling and recognizing isolated signs, which inherently performs model selection to optimize the number of states for each sign separately during recognition. Our experiments on three different datasets, namely, German sign language DGS dataset, Turkish sign language HospiSign dataset and Chalearn14 dataset show that the proposed approach achieves better sign language or gesture recognition systems in comparison to the approach of selecting or presetting the number of HMM states based on k-means, and yields systems that perform competitive to the case where the number of states are determined based on the test set performance.
Automated Parsing of Interlinear Glossed Text from Page Images of Grammatical Descriptions

Linguists seek insight from all human languages, however accessing information from most of the full store of extant global linguistic descriptions is not easy. One of the most common kinds of information that linguists have documented is vernacular sentences, as recorded in descriptive grammars. Typically these sentences are formatted as interlinear glossed text (IGT). Most descriptive grammars, however, exist only as hardcopy or scanned pdf documents. Consequently, parsing IGTs in scanned grammars is a priority, in order to significantly increase the volume of documented linguistic information that is readily accessible. Here we demonstrate fundamental viability for a technology that can assist in making a large number of linguistic data sources machine readable: the automated identification and parsing of interlinear glossed text from scanned page images. For example, we attain high median precision and recall (>0.95) in the identification of examples sentences in IGT format. Our results will be of interest to those who are keen to see more of the existing documentation of human language, especially for less-resourced and endangered languages, become more readily accessible.
IIIT-H TEMD Semi-Natural Emotional Speech Database from Professional Actors and Non-Actors

A fundamental essence for emotional speech analysis towards emotion recognition is a good database. Database collected from natural scenarios consists of spontaneous emotions, but there are several issues in collection of such database. Other than the privacy and legal related concerns, there is no control over environment at the background. As it is difficult to collect data from natural scenarios, many research groups have collected data from semi-natural or designed procedures. In this paper, a new emotional speech database named IIIT-H TEMD (International Institute of Information Technology-Hyderabad Telugu Emotional Database) is collected using designed drama situations from actors and non-actors. Utterances are manually annotated using a hybrid strategy by giving the context to one of the listeners. As some of the data collection studies in the literature recommend for actors, analysis of actors data versus non-actors data is carried out for their significance. The total size of the dataset is about 5 hours, which makes it an useful resource for the emotional speech analysis.
Improving the Precision of Natural Textual Entailment Problem Datasets

In this paper, we propose a method to modify natural textual entailment problem datasets so that they better reflect a more precise notion of entailment. We apply this method to a subset of the Recognizing Textual Entailment datasets. We thus obtain a new corpus of entailment problems, which has the following three characteristics: 1. it is precise (does not leave out implicit hypotheses) 2. it is based on “real-world” texts (i.e. most of the premises were written for purposes other than testing textual entailment). 3. its size is 150. Broadly, the method that we employ is to make any missing hypotheses explicit using a crowd of experts. We discuss the relevance of our method in improving existing NLI datasets to be more fit for precise reasoning and we argue that this corpus can be the basis a first step towards wide-coverage testing of precise natural-language inference systems.
Chinese Discourse Parsing: Model and Evaluation

Chinese discourse parsing, which aims to identify the hierarchical relationships of Chinese elementary discourse units, has not yet a consistent evaluation metric. Although Parseval is commonly used, variations of evaluation differ from three aspects: micro vs. macro F1 scores, binary vs. multiway ground truth, and left-heavy vs. right-heavy binarization. In this paper, we first propose a neural network model that unifies a pre-trained transformer and CKY-like algorithm, and then compare it with the previous models with different evaluation scenarios. The experimental results show that our model outperforms the previous systems. We conclude that (1) the pre-trained context embedding provides effective solutions to deal with implicit semantics in Chinese texts, and (2) using multiway ground truth is helpful since different binarization approaches lead to significant differences in performance.
Discovering Biased News Articles Leveraging Multiple Human Annotations

Unbiased and fair reporting is an integral part of ethical journalism. Yet, political propaganda and one-sided views can be found in the news and can cause distrust in media. Both accidental and deliberate political bias affect the readers and shape their views. We contribute to a trustworthy media ecosystem by automatically identifying politically biased news articles. We introduce novel corpora annotated by two communities, i.e., domain experts and crowd workers, and we also consider automatic article labels inferred by the newspapers’ ideologies. Our goal is to compare domain experts to crowd workers and also to prove that media bias can be detected automatically. We classify news articles with a neural network and we also improve our performance in a self-supervised manner.
32. Stochastic Optimization Problems in Telecommunications

We survey different optimization problems under uncertainty which arise in telecommunications. Three levels of decisions are distinguished: design of structural elements of telecommunication networks, top level design of telecommunication networks and design of optimal policies of telecommunication enterprize. Examples of typical problems from each level show that the stochastic programming paradigm is a powerful approach for solving telecommunication design problems.
Annotation of Adverse Drug Reactions in Patients’ Weblogs

Adverse drug reactions are a severe problem that significantly degrade quality of life, or even threaten the life of patients. Patient-generated texts available on the web have been gaining attention as a promising source of information in this regard. While previous studies annotated such patient-generated content, they only reported on limited information, such as whether a text described an adverse drug reaction or not. Further, they only annotated short texts of a few sentences crawled from online forums and social networking services. The dataset we present in this paper is unique for the richness of annotated information, including detailed descriptions of drug reactions with full context. We crawled patient’s weblog articles shared on an online patient-networking platform and annotated the effects of drugs therein reported. We identified spans describing drug reactions and assigned labels for related drug names, standard codes for the symptoms of the reactions, and types of effects. As a first dataset, we annotated 677 drug reactions with these detailed labels based on 169 weblog articles by Japanese lung cancer patients. Our annotation dataset is made publicly available at our web site (https://yukiar.github.io/adr-jp/) for further research on the detection of adverse drug reactions and more broadly, on patient-generated text processing.
POSITIONAL DISTRIBUTION OF FATTY ACIDS IN THE FATS OF A POLAR BEAR AND A SEAL

Fats of a polar bear and a seal were analyzed. It was found in both samples that the polyenoic fatty acids preferably occupy the α-positions and, especially, position 3. Position 2 contains short acids, and position 1 contains the longer and more saturated acids. It is suggested that this distribution pattern is typical for mammals feeding on marine fats.
An Emotional Mess! Deciding on a Framework for Building a Dutch Emotion-Annotated Corpus

Seeing the myriad of existing emotion models, with the categorical versus dimensional opposition the most important dividing line, building an emotion-annotated corpus requires some well thought-out strategies concerning framework choice. In our work on automatic emotion detection in Dutch texts, we investigate this problem by means of two case studies. We find that the labels joy, love, anger, sadness and fear are well-suited to annotate texts coming from various domains and topics, but that the connotation of the labels strongly depends on the origin of the texts. Moreover, it seems that information is lost when an emotional state is forcedly classified in a limited set of categories, indicating that a bi-representational format is desirable when creating an emotion corpus.
MSD-1030: A Well-built Multi-Sense Evaluation Dataset for Sense Representation Models

Sense embedding models handle polysemy by giving each distinct meaning of a word form a separate representation. They are considered improvements over word models, and their effectiveness is usually judged with benchmarks such as semantic similarity datasets. However, most of these datasets are not designed for evaluating sense embeddings. In this research, we show that there are at least six concerns about evaluating sense embeddings with existing benchmark datasets, including the large proportions of single-sense words and the unexpected inferior performance of several multi-sense models to their single-sense counterparts. These observations call into serious question whether evaluations based on these datasets can reflect the sense model’s ability to capture different meanings. To address the issues, we propose the Multi-Sense Dataset (MSD-1030), which contains a high ratio of multi-sense word pairs. A series of analyses and experiments show that MSD-1030 serves as a more reliable benchmark for sense embeddings. The dataset is available at http://nlg.csie. ntu.edu. tw/nlpresource/MSD-1030/.
Augmented Prompt Selection for Evaluation of Spontaneous Speech Synthesis

By definition, spontaneous speech is unscripted and created on the fly by the speaker. It is dramatically different from read speech, where the words are authored as text before they are spoken. Spontaneous speech is emergent and transient, whereas text read out loud is pre-planned. For this reason, it is unsuitable to evaluate the usability and appropriateness of spontaneous speech synthesis by having it read out written texts sampled from for example newspapers or books. Instead, we need to use transcriptions of speech as the target - something that is much less readily available. In this paper, we introduce Starmap, a tool allowing developers to select a varied, representative set of utterances from a spoken genre, to be used for evaluation of TTS for a given domain. The selection can be done from any speech recording, without the need for transcription. The tool uses interactive visualisation of prosodic features with t-SNE, along with a tree-based algorithm to guide the user through thousands of utterances and ensure coverage of a variety of prompts. A listening test has shown that with a selection of genre-specific utterances, it is possible to show significant differences across genres between two synthetic voices built from spontaneous speech.
NTeALan Dictionaries Platforms: An Example Of Collaboration-Based Model

Nowadays the scarcity and dispersion of open-source NLP resources and tools in and for African languages make it difficult for researchers to truly fit these languages into current algorithms of artificial intelligence, resulting in the stagnation of these numerous languages, as far as technological progress is concerned. Created in 2017, with the aim of building communities of voluntary contributors around African native and/or national languages, cultures, NLP technologies and artificial intelligence, the NTeALan association has set up a series of web collaborative platforms intended to allow the aforementioned communities to create and manage their own lexicographic and linguistic resources. This paper aims at presenting the first versions of three lexicographic platforms that we developed in and for African languages: the REST/GraphQL API for saving lexicographic resources, the dictionary management platform and the collaborative dictionary platform. We also describe the data representation format used for these resources. After experimenting with a few dictionaries and looking at users feedback, we are convinced that only collaboration-based approaches and platforms can effectively respond to challenges of producing quality resources in and for African native and/or national languages.
Alignment Data base for a Sign Language Concordancer

This article deals with elaborating a data base of alignments of parallel Franch-LSF segments. This data base is meant to be searched using a concordancer which we are also designing. We wish to equip Sign Language translators with tools similar to those used in text-to-text translation. To do so, we need language resources to feed them. Already existing Sign Language corpora can be found, but do not match our needs: working around a Sign Language concordancer, the corpus must be a parallel one and provide various examples of vocabulary and grammatical construction. We started with a parallel corpus of 40 short news and 120 SL videos , which we aligned manually by segments of various length. We described the methodology we used, how we define our segments and alignments. The last part concerns how we hope to allow the data base to keep growing in a near future.
Streaming Language-Specific Twitter Data with Optimal Keywords

The Twitter Streaming API has been used to create language-specific corpora with varying degrees of success. Selecting a filter of frequent yet distinct keywords for German resulted in a near-complete collection of German tweets. This method is promising as it keeps within Twitter endpoint limitations and could be applied to other languages besides German. But so far no research has compared methods for selecting optimal keywords for this task. This paper proposes a method for finding optimal key phrases based on a greedy solution to the maximum coverage problem. We generate candidate key phrases for the 50 most frequent languages on Twitter. Candidates are then iteratively selected based on a variety of scoring functions applied to their coverage of target tweets. Selecting candidates based on the scoring function that exponentiates the precision of a key phrase and weighs it by recall achieved the best results overall. Some target languages yield lower results than what could be expected from their prevalence on Twitter. Upon analyzing the errors, we find that these are languages that are very close to more prevalent languages. In these cases, key phrases that limit finding the competitive language are selected, and overall recall on the target language also decreases. We publish the resulting optimized lists for each language as a resource. The code to generate lists for other research objectives is also supplied.
A Spelling Correction Corpus for Multiple Arabic Dialects

Arabic dialects are the non-standard varieties of Arabic commonly spoken – and increasingly written on social media – across the Arab world. Arabic dialects do not have standard orthographies, a challenge for natural language processing applications. In this paper, we present the MADAR CODA Corpus, a collection of 10,000 sentences from five Arabic city dialects (Beirut, Cairo, Doha, Rabat, and Tunis) represented in the Conventional Orthography for Dialectal Arabic (CODA) in parallel with their raw original form. The sentences come from the Multi-Arabic Dialect Applications and Resources (MADAR) Project and are in parallel across the cities (2,000 sentences from each city). This publicly available resource is intended to support research on spelling correction and text normalization for Arabic dialects. We present results on a bootstrapping technique we use to speed up the CODA annotation, as well as on the degree of similarity across the dialects before and after CODA annotation.
DAICT: A Dialectal Arabic Irony Corpus Extracted from Twitter

Identifying irony in user-generated social media content has a wide range of applications; however to date Arabic content has received limited attention. To bridge this gap, this study builds a new open domain Arabic corpus annotated for irony detection. We query Twitter using irony-related hashtags to collect ironic messages, which are then manually annotated by two linguists according to our working definition of irony. Challenges which we have encountered during the annotation process reflect the inherent limitations of Twitter messages interpretation, as well as the complexity of Arabic and its dialects. Once published, our corpus will be a valuable free resource for developing open domain systems for automatic irony recognition in Arabic language and its dialects in social media text.
BLISS: An Agent for Collecting Spoken Dialogue Data about Health and Well-being

An important objective in health-technology is the ability to gather information about people’s well-being. Structured interviews can be used to obtain this information, but are time-consuming and not scalable. Questionnaires provide an alternative way to extract such information, though typically lack depth. In this paper, we present our first prototype of the BLISS agent, an artificial intelligent agent which intends to automatically discover what makes people happy and healthy. The goal of Behaviour-based Language-Interactive Speaking Systems (BLISS) is to understand the motivations behind people’s happiness by conducting a personalized spoken dialogue based on a happiness model. We built our first prototype of the model to collect 55 spoken dialogues, in which the BLISS agent asked questions to users about their happiness and well-being. Apart from a description of the BLISS architecture, we also provide details about our dataset, which contains over 120 activities and 100 motivations and is made available for usage.
SOLO: A Corpus of Tweets for Examining the State of Being Alone

The state of being alone can have a substantial impact on our lives, though experiences with time alone diverge significantly among individuals. Psychologists distinguish between the concept of solitude, a positive state of voluntary aloneness, and the concept of loneliness, a negative state of dissatisfaction with the quality of one’s social interactions. Here, for the first time, we conduct a large-scale computational analysis to explore how the terms associated with the state of being alone are used in online language. We present SOLO (State of Being Alone), a corpus of over 4 million tweets collected with query terms solitude, lonely, and loneliness. We use SOLO to analyze the language and emotions associated with the state of being alone. We show that the term solitude tends to co-occur with more positive, high-dominance words (e.g., enjoy, bliss) while the terms lonely and loneliness frequently co-occur with negative, low-dominance words (e.g., scared, depressed), which confirms the conceptual distinctions made in psychology. We also show that women are more likely to report on negative feelings of being lonely as compared to men, and there are more teenagers among the tweeters that use the word lonely than among the tweeters that use the word solitude.
Learning the Human Judgment for the Automatic Evaluation of Chatbot

It is hard to evaluate the quality of the generated text by a generative dialogue system. Currently, dialogue evaluation relies on human judges to label the quality of the generated text. It is not a reusable mechanism that can give consistent evaluation for system developers. We believe that it is easier to get consistent results on comparing two generated dialogue by two systems and it is hard to give a consistent quality score on only one system at a time. In this paper, we propose a machine learning approach to reduce the effort of human evaluation by learning the human judgment on comparing two dialogue systems. Training from the human labeling result, the evaluation model learns which generative models is better in each dialog context. Thus, it can be used for system developers to compare the fine-tuned models over and over again without the human labor. In our experiment we find the agreement between the learned model and human judge is 70%. The experiment is conducted on comparing two attention based GRU-RNN generative models.