pwc-archive/papers-with-abstracts huggingface dataset:
Logic-Guided Message Generation from Raw Real-Time Sensor Data
Authors: Ernie Chang, Alisa Kovtunova, Stefan Borgwardt, Vera Demberg, Kathryn Chapman, Hui-Syuan Yeh
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Text Generation

Natural language generation in real-time settings with raw sensor data is a challenging task. We find that formulating the task as an end-to-end problem leads to two major challenges in content selection – the sensor data is both redundant and diverse across environments, thereby making it hard for the encoders to select and reason on the data. We here present a new corpus for a specific domain that instantiates these properties. It includes handover utterances that an assistant for a semi-autonomous drone uses to communicate with humans during the drone flight. The corpus consists of sensor data records and utterances in 8 different environments. As a structured intermediary representation between data records and text, we explore the use of description logic (DL). We also propose a neural generation model that can alert the human pilot of the system state and environment in preparation of the handover of control.
The Bull and the Bear: Summarizing Stock Market Discussions
Authors: Ayush Kumar, Dhyey Jani, Jay Shah, Devanshu Thakar, Varun Jain, Mayank Singh
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Abstractive Text Summarization

Stock market investors debate and heavily discuss stock ideas, investing strategies, news and market movements on social media platforms. The discussions are significantly longer in length and require extensive domain expertise for understanding. In this paper, we curate such discussions and construct a first-of-its-kind of abstractive summarization dataset. Our curated dataset consists of 7888 Reddit posts and manually constructed summaries for 400 posts. We robustly evaluate the summaries and conduct experiments on SOTA summarization tools to showcase their limitations. We plan to make the dataset publicly available. The sample dataset is available here: https://dhyeyjani.github.io/RSMC
Combination of Contextualized and Non-Contextualized Layers for Lexical Substitution in French
Authors: Kévin Espasa, Emmanuel Morin, Olivier Hamon
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Language Modelling, Sentence

Lexical substitution task requires to substitute a target word by candidates in a given context. Candidates must keep meaning and grammatically of the sentence. The task, introduced in the SemEval 2007, has two objectives. The first objective is to find a list of substitutes for a target word. This list of substitutes can be obtained with lexical resources like WordNet or generated with a pre-trained language model. The second objective is to rank these substitutes using the context of the sentence. Most of the methods use vector space models or more recently embeddings to rank substitutes. Embedding methods use high contextualized representation. This representation can be over contextualized and in this way overlook good substitute candidates which are more similar on non-contextualized layers. SemDis 2014 introduced the lexical substitution task in French. We propose an application of the state-of-the-art method based on BERT in French and a novel method using contextualized and non-contextualized layers to increase the suggestion of words having a lower probability in a given context but that are more semantically similar. Experiments show our method increases the BERT based system on the OOT measure but decreases on the BEST measure in the SemDis 2014 benchmark.
CATAMARAN: A Cross-lingual Long Text Abstractive Summarization Dataset
Authors: Zheng Chen, Hongyu Lin
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Abstractive Text Summarization, Articles, Cross-Lingual Abstractive Summarization

Cross-lingual summarization, which produces the summary in one language from a given source document in another language, could be extremely helpful for humans to obtain information across the world. However, it is still a little-explored task due to the lack of datasets. Recent studies are primarily based on pseudo-cross-lingual datasets obtained by translation. Such an approach would inevitably lead to the loss of information in the original document and introduce noise into the summary, thus hurting the overall performance. In this paper, we present CATAMARAN, the first high-quality cross-lingual long text abstractive summarization dataset. It contains about 20,000 parallel news articles and corresponding summaries, all written by humans. The average lengths of articles are 1133.65 for English articles and 2035.33 for Chinese articles, and the average lengths of the summaries are 26.59 and 70.05, respectively. We train and evaluate an mBART-based cross-lingual abstractive summarization model using our dataset. The result shows that, compared with mono-lingual systems, the cross-lingual abstractive summarization system could also achieve solid performance.
Tackling Irony Detection using Ensemble Classifiers
Authors: Christoph Turban, Udo Kruschwitz
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Binary Classification, Data Augmentation, Multi-class Classification

Automatic approaches to irony detection have been of interest to the NLP community for a long time, yet, state-of-the-art approaches still fall way short of what one would consider a desirable performance. In part this is due to the inherent difficulty of the problem. However, in recent years ensembles of transformer-based approaches have emerged as a promising direction to push the state of the art forward in a wide range of NLP applications. A different, more recent, development is the automatic augmentation of training data. In this paper we will explore both these directions for the task of irony detection in social media. Using the common SemEval 2018 Task 3 benchmark collection we demonstrate that transformer models are well suited in ensemble classifiers for the task at hand. In the multi-class classification task we observe statistically significant improvements over strong baselines. For binary classification we achieve performance that is on par with state-of-the-art alternatives. The examined data augmentation strategies showed an effect, but are not decisive for good results.
Automatic Construction of an Annotated Corpus with Implicit Aspects
Authors: Aye Aye Mar, Kiyoaki Shirai
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a task that involves classifying the polarity of aspects of the products or services described in users’ reviews. Most previous work on ABSA has focused on explicit aspects, which appear as explicit words or phrases in the sentences of the review. However, users often express their opinions toward the aspects indirectly or implicitly, in which case the specific name of an aspect does not appear in the review. The current datasets used for ABSA are mainly annotated with explicit aspects. This paper proposes a novel method for constructing a corpus that is automatically annotated with implicit aspects. The main idea is that sentences containing explicit and implicit aspects share a similar context. First, labeled sentences with explicit aspects and unlabeled sentences that include implicit aspects are collected. Next, clustering is performed on these sentences so that similar sentences are merged into the same cluster. Finally, the explicit aspects are propagated to the unlabeled sentences in the same cluster, in order to construct a labeled dataset containing implicit aspects. The results of our experiments on mobile phone reviews show that our method of identifying the labels of implicit aspects achieves a maximum accuracy of 82%.
Annotation of Valence Unfolding in Spoken Personal Narratives
Authors: Aniruddha Tammewar, Franziska Braun, Gabriel Roccabruna, Sebastian Bayerl, Korbinian Riedhammer, Giuseppe Riccardi
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

Personal Narrative (PN) is the recollection of individuals’ life experiences, events, and thoughts along with the associated emotions in the form of a story. Compared to other genres such as social media texts or microblogs, where people write about experienced events or products, the spoken PNs are complex to analyze and understand. They are usually long and unstructured, involving multiple and related events, characters as well as thoughts and emotions associated with events, objects, and persons. In spoken PNs, emotions are conveyed by changing the speech signal characteristics as well as the lexical content of the narrative. In this work, we annotate a corpus of spoken personal narratives, with the emotion valence using discrete values. The PNs are segmented into speech segments, and the annotators annotate them in the discourse context, with values on a 5-point bipolar scale ranging from -2 to +2 (0 for neutral). In this way, we capture the unfolding of the PNs events and changes in the emotional state of the narrator. We perform an in-depth analysis of the inter-annotator agreement, the relation between the label distribution w.r.t. the stimulus (positive/negative) used for the elicitation of the narrative, and compare the segment-level annotations to a baseline continuous annotation. We find that the neutral score plays an important role in the agreement. We observe that it is easy to differentiate the positive from the negative valence while the confusion with the neutral label is high. Keywords: Personal Narratives, Emotion Annotation, Segment Level Annotation
A Large-Scale Japanese Dataset for Aspect-based Sentiment Analysis
Authors: Yuki Nakayama, Koji Murakami, Gautam Kumar, Sudha Bhingardive, Ikuko Hardaway
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis

There has been significant progress in the field of sentiment analysis. However, aspect-based sentiment analysis (ABSA) has not been explored in the Japanese language even though it has a huge scope in many natural language processing applications such as 1) tracking sentiment towards products, movies, politicians etc; 2) improving customer relation models. The main reason behind this is that there is no standard Japanese dataset available for ABSA task. In this paper, we present the first standard Japanese dataset for the hotel reviews domain. The proposed dataset contains 53,192 review sentences with seven aspect categories and two polarity labels. We perform experiments on this dataset using popular ABSA approaches and report error analysis. Our experiments show that contextual models such as BERT works very well for the ABSA task in the Japanese language and also show the need to focus on other NLP tasks for better performance through our error analysis.
A Japanese Dataset for Subjective and Objective Sentiment Polarity Classification in Micro Blog Domain
Authors: Haruya Suzuki, Yuto Miyauchi, Kazuki Akiyama, Tomoyuki Kajiwara, Takashi Ninomiya, Noriko Takemura, Yuta Nakashima, Hajime Nagahara
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Benchmarking, Emotion Recognition, Sentiment Analysis

We annotate 35,000 SNS posts with both the writer’s subjective sentiment polarity labels and the reader’s objective ones to construct a Japanese sentiment analysis dataset. Our dataset includes intensity labels (none, weak, medium, and strong) for each of the eight basic emotions by Plutchik (joy, sadness, anticipation, surprise, anger, fear, disgust, and trust) as well as sentiment polarity labels (strong positive, positive, neutral, negative, and strong negative). Previous studies on emotion analysis have studied the analysis of basic emotions and sentiment polarity independently. In other words, there are few corpora that are annotated with both basic emotions and sentiment polarity. Our dataset is the first large-scale corpus to annotate both of these emotion labels, and from both the writer’s and reader’s perspectives. In this paper, we analyze the relationship between basic emotion intensity and sentiment polarity on our dataset and report the results of benchmarking sentiment polarity classification.
Complementary Learning of Aspect Terms for Aspect-based Sentiment Analysis
Authors: Han Qin, Yuanhe Tian, Fei Xia, Yan Song
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentence, Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity towards a given aspect term in a sentence on the fine-grained level, which usually requires a good understanding of contextual information, especially appropriately distinguishing of a given aspect and its contexts, to achieve good performance. However, most existing ABSA models pay limited attention to the modeling of the given aspect terms and thus result in inferior results when a sentence contains multiple aspect terms with contradictory sentiment polarities. In this paper, we propose to improve ABSA by complementary learning of aspect terms, which serves as a supportive auxiliary task to enhance ABSA by explicitly recovering the aspect terms from each input sentence so as to better understand aspects and their contexts. Particularly, a discriminator is also introduced to further improve the learning process by appropriately balancing the impact of aspect recovery to sentiment prediction. Experimental results on five widely used English benchmark datasets for ABSA demonstrate the effectiveness of our approach, where state-of-the-art performance is observed on all datasets.
Deep One-Class Hate Speech Detection Model
Authors: Saugata Bose, Dr. Guoxin Su
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Benchmarking, Binary Classification, Classification, Hate Speech Detection, model

Hate speech detection for social media posts is considered as a binary classification problem in existing approaches, largely neglecting distinct attributes of hate speeches from other sentimental types such as “aggressive” and “racist”. As these sentimental types constitute a significant major portion of data, the classification performance is compromised. Moreover, those classifiers often do not generalize well across different datasets due to a relatively small number of hate-class samples. In this paper, we adopt a one-class perspective for hate speech detection, where the detection classifier is trained with hate-class samples only. Our model employs a BERT-BiLSTM module for feature extraction and a one-class SVM for classification. A comprehensive evaluation with four benchmarking datasets demonstrates the better performance of our model than existing approaches, as well as the advantage of training our model with a combination of the four datasets.
Opinions in Interactions : New Annotations of the SEMAINE Database
Authors: Valentin Barriere, Slim Essid, Chloé Clavel
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

In this paper, we present the process we used in order to collect new annotations of opinions over the multimodal corpus SEMAINE composed of dyadic interactions. The dataset had already been annotated continuously in two affective dimensions related to the emotions: Valence and Arousal. We annotated the part of SEMAINE called Solid SAL composed of 79 interactions between a user and an operator playing the role of a virtual agent designed to engage a person in a sustained, emotionally colored conversation. We aligned the audio at the word level using the available high-quality manual transcriptions. The annotated dataset contains 5627 speech turns for a total of 73,944 words, corresponding to 6 hours 20 minutes of dyadic interactions. Each interaction has been labeled by three annotators at the speech turn level following a three-step process. This method allows us to obtain a precise annotation regarding the opinion of a speaker. We obtain thus a dataset dense in opinions, with more than 48% of the annotated speech turns containing at least one opinion. We then propose a new baseline for the detection of opinions in interactions improving slightly a state of the art model with RoBERTa embeddings. The obtained results on the database are promising with a F1-score at 0.72.
Pars-ABSA: a Manually Annotated Aspect-based Sentiment Analysis Benchmark on Farsi Product Reviews
Authors: Taha Shangipour ataei, Kamyar Darvishi, Soroush Javdan, Behrouz Minaei-Bidgoli, Sauleh Eetemadi
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentence, Sentiment Analysis, Transfer Learning

Due to the increased availability of online reviews, sentiment analysis witnessed a thriving interest from researchers. Sentiment analysis is a computational treatment of sentiment used to extract and understand the opinions of authors. While many systems were built to predict the sentiment of a document or a sentence, many others provide the necessary detail on various aspects of the entity (i.e., aspect-based sentiment analysis). Most of the available data resources were tailored to English and the other popular European languages. Although Farsi is a language with more than 110 million speakers, to the best of our knowledge, there is a lack of proper public datasets on aspect-based sentiment analysis for Farsi. This paper provides a manually annotated Farsi dataset, Pars-ABSA, annotated and verified by three native Farsi speakers. The dataset consists of 5,114 positive, 3,061 negative and 1,827 neutral data samples from 5,602 unique reviews. Moreover, as a baseline, this paper reports the performance of some aspect-based sentiment analysis methods focusing on transfer learning on Pars-ABSA.
HindiMD: A Multi-domain Corpora for Low-resource Sentiment Analysis
Authors: Mamta ., Asif Ekbal, Pushpak Bhattacharyya, Tista Saha, Alka Kumar, Shikha Srivastava
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Sentiment Analysis

Social media platforms such as Twitter have evolved into a vast information sharing platform, allowing people from a variety of backgrounds and expertise to share their opinions on numerous events such as terrorism, narcotics and many other social issues. People sometimes misuse the power of social media for their agendas, such as illegal trades and negatively influencing others. Because of this, sentiment analysis has won the interest of a lot of researchers to widely analyze public opinion for social media monitoring. Several benchmark datasets for sentiment analysis across a range of domains have been made available, especially for high-resource languages. A few datasets are available for low-resource Indian languages like Hindi, such as movie reviews and product reviews, which do not address the current need for social media monitoring. In this paper, we address the challenges of sentiment analysis in Hindi and socially relevant domains by introducing a balanced corpus annotated with the sentiment classes, viz. positive, negative and neutral. To show the effective usage of the dataset, we build several deep learning based models and establish them as the baselines for further research in this direction.
Sentiment Analysis of Homeric Text: The 1st Book of Iliad
Authors: John Pavlopoulos, Alexandros Xenos, Davide Picca
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Language Modeling, Language Modelling, Sentiment Analysis, Time Series, Time Series Analysis

Sentiment analysis studies are focused more on online customer reviews or social media, and less on literary studies. The problem is greater for ancient languages, where the linguistic expression of sentiments may diverge from modern linguistic forms. This work presents the outcome of a sentiment annotation task of the first Book of Iliad, an ancient Greek poem. The annotators were provided with verses translated into modern Greek and they annotated the perceived emotions and sentiments verse by verse. By estimating the fraction of annotators that found a verse as belonging to a specific sentiment class, we model the poem’s perceived sentiment as a multi-variate time series. By experimenting with a state of the art deep learning masked language model, pre-trained on modern Greek and fine-tuned to estimate the sentiment of our data, we registered a mean squared error of 0.063. This low error indicates that sentiment estimators built on our dataset can potentially be used as mechanical annotators, hence facilitating the distant reading of Homeric text. Our dataset is released for public use.
GujMORPH - A Dataset for Creating Gujarati Morphological Analyzer
Authors: Jatayu Baxi, Brijesh Bhatt
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6

Computational morphology deals with the processing of a language at the word level. A morphological analyzer is a key linguistic word-level tool that returns all the constituent morphemes and their grammatical categories associated with a particular word form. For the highly inflectional and low resource languages, the creation of computational morphology-related tools is a challenging task due to the unavailability of underlying key resources. In this paper, we discuss the creation of an annotated morphological dataset- GujMORPH for the Gujarati - an indo-aryan language. For the creation of this dataset, we studied language grammar, word formation rules, and suffix attachments in depth. This dataset contains 16,527 unique inflected words along with their morphological segmentation and grammatical feature tagging information. It is a first of its kind dataset for the Gujarati language and can be used to develop morphological analyzer and generator models. The dataset is annotated in the standard Unimorph schema and evaluated on the baseline system. We also describe the tool used to annotate the data in the standard format. The dataset is released publicly along with the library. Using this library, the data can be obtained in a format that can be directly used to train any machine learning model.
Building Large-Scale Japanese Pronunciation-Annotated Corpora for Reading Heteronymous Logograms
Authors: Fumikazu Sato, Naoki Yoshinaga, Masaru Kitsuregawa
Date: 2022-06-01 00:00:00
Proceeding: LREC 2022 6
Tasks: Sentence

Although screen readers enable visually impaired people to read written text via speech, the ambiguities in pronunciations of heteronyms cause wrong reading, which has a serious impact on the text understanding. Especially in Japanese, there are many common heteronyms expressed by logograms (Chinese characters or kanji) that have totally different pronunciations (and meanings). In this study, to improve the accuracy of pronunciation prediction, we construct two large-scale Japanese corpora that annotate kanji characters with their pronunciations. Using existing language resources on i) book titles compiled by the National Diet Library and ii) the books in a Japanese digital library called Aozora Bunko and their Braille translations, we develop two large-scale pronunciation-annotated corpora for training pronunciation prediction models. We first extract sentence-level alignments between the Aozora Bunko text and its pronunciation converted from the Braille data. We then perform dictionary-based pattern matching based on morphological dictionaries to find word-level pronunciation alignments. We have ultimately obtained the Book Title corpus with 336M characters (16.4M book titles) and the Aozora Bunko corpus with 52M characters (1.6M sentences). We analyzed pronunciation distributions for 203 common heteronyms, and trained a BERT-based pronunciation prediction model for 93 heteronyms, which achieved an average accuracy of 0.939.