pwc-archive/papers-with-abstracts huggingface dataset:
Using Word Embedding to Reveal Monetary Policy Explanation Changes
Authors: Akira Matsui, Xiang Ren, Emilio Ferrara
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ECONLP) 2021 11
Tasks: Sentiment Analysis

Documents have been an essential tool of communication for governments to announce their policy operations. Most policy announcements have taken the form of text to inform their new policies or changes to the public. To understand such policymakers’ communication, many researchers exploit published policy documents. However, the methods well-used in other research domains such as sentiment analysis or topic modeling are not suitable for studying policy communications. Their training corpora and methods are not for policy documents where technical terminologies are used, and sentiment expressions are refrained. We leverage word embedding techniques to extract semantic changes in the monetary policy documents. Our empirical study shows that the policymaker uses different semantics according to the type of documents when they change their policy.
To What Extent Can English-as-a-Second Language Learners Read Economic News Texts?
Authors: Yo Ehara
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ECONLP) 2021 11
Tasks: Decision Making, Translation

In decision making in the economic field, an especially important requirement is to rapidly understand news to absorb ever-changing economic situations. Given that most economic news is written in English, the ability to read such information without waiting for a translation is particularly valuable in economics in contrast to other fields. In consideration of this issue, this research investigated the extent to which non-native English speakers are able to read economic news to make decisions accordingly – an issue that has been rarely addressed in previous studies. Using an existing standard dataset as training data, we created a classifier that automatically evaluates the readability of text with high accuracy for English learners. Our assessment of the readability of an economic news corpus revealed that most news texts can be read by intermediate English learners. We also found that in some cases, readability varies considerably depending on the knowledge of certain words specific to the economic field.
Effective Use of Graph Convolution Network and Contextual Sub-Tree for Commodity News Event Extraction
Authors: Meisin Lee, Lay-Ki Soon, Eu-Gene Siew
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ECONLP) 2021 11
Tasks: Event Extraction, Language Modeling, Language Modelling

Event extraction in commodity news is a less researched area as compared to generic event extraction. However, accurate event extraction from commodity news is useful in abroad range of applications such as under-standing event chains and learning event-event relations, which can then be used for commodity price prediction. The events found in commodity news exhibit characteristics different from generic events, hence posing a unique challenge in event extraction using existing methods. This paper proposes an effective use of Graph Convolutional Networks(GCN) with a pruned dependency parse tree, termed contextual sub-tree, for better event ex-traction in commodity news. The event ex-traction model is trained using feature embed-dings from ComBERT, a BERT-based masked language model that was produced through domain-adaptive pre-training on a commodity news corpus. Experimental results show the efficiency of the proposed solution, which out-performs existing methods with F1 scores as high as 0.90. Furthermore, our pre-trained language model outperforms GloVe by 23%, and BERT and RoBERTa by 7% in terms of argument roles classification. For the goal of re-producibility, the code and trained models are made publicly available.
Cryptocurrency Day Trading and Framing Prediction in Microblog Discourse
Authors: Anna Paula Pawlicka Maule, Kristen Johnson
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ECONLP) 2021 11
Tasks: Deep Clustering, Language Modeling, Language Modelling, Prediction

With 56 million people actively trading and investing in cryptocurrency online and globally in 2020, there is an increasing need for automatic social media analysis tools to help understand trading discourse and behavior. In this work, we present a dual natural language modeling pipeline which leverages language and social network behaviors for the prediction of cryptocurrency day trading actions and their associated framing patterns. This pipeline first predicts if tweets can be used to guide day trading behavior, specifically if a cryptocurrency investor should buy, sell, or hold their cryptocurrencies in order to make a profit. Next, tweets are input to an unsupervised deep clustering approach to automatically detect trading framing patterns. Our contributions include the modeling pipeline for this novel task, a new Cryptocurrency Tweets Dataset compiled from influential accounts, and a Historical Price Dataset. Our experiments show that our approach achieves an 88.78% accuracy for day trading behavior prediction and reveals framing fluctuations prior to and during the COVID-19 pandemic that could be used to guide investment actions.
Extracting Economic Signals from Central Bank Speeches
Authors: Maximilian Ahrens, Michael McMahon
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ECONLP) 2021 11

Estimating the effects of monetary policy is one of the fundamental research questions in monetary economics. Many economies are facing ultra-low interest rate environments ever since the global financial crisis of 2007-9. The Covid pandemic recently reinforced this situation. In the US and Europe, interest rates are close to (or even below) zero, which limits the scope of traditional monetary policy measures for central banks. Dedicated central bank communication has hence become an increasingly important tool to steer and control market expectations these days. However, incorporating central bank language directly as features into economic models is still a very nascent research area. In particular, the content and effect of central bank speeches has been mostly neglected from monetary policy modelling so far. With our paper, we aim to provide to the research community a novel, monetary policy shock series based on central bank speeches. We use a supervised topic modeling approach that can deal with text as well as numeric covariates to estimate a monetary policy signal dispersion index along three key economic dimensions: GDP, CPI and unemployment. This “dispersion shock” series is not only more frequent than series that classically focus on policy announcement dates, it also opens up the possibility of answering new questions that have up until now been difficult to analyse. For example, do markets form different expectations when facing a “cacophony of policy voices”? Our initial findings for the US point towards the fact that more dispersed or incongruent monetary policy stance communication in the build up to Federal Open Market Committee (FOMC) meetings might be associated with stronger subsequent market surprises at FOMC policy announcement time.
Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory
Authors: Shamik Roy, Dan Goldwasser
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SocialNLP) 2021 6
Tasks: Relational Reasoning, Sentence

The Moral Foundation Theory suggests five moral foundations that can capture the view of a user on a particular issue. It is widely used to identify sentence-level sentiment. In this paper, we study the Moral Foundation Theory in tweets by US politicians on two politically divisive issues - Gun Control and Immigration. We define the nuanced stance of politicians on these two topics by the grades given by related organizations to the politicians. First, we identify moral foundations in tweets from a huge corpus using deep relational learning. Then, qualitative and quantitative evaluations using the corpus show that there is a strong correlation between the moral foundation usage and the politicians’ nuanced stance on a particular topic. We also found substantial differences in moral foundation usage by different political parties when they address different entities. All of these results indicate the need for more intense research in this area.
Content-based Stance Classification of Tweets about the 2020 Italian Constitutional Referendum
Authors: Marco Di Giovanni, Marco Brambilla
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SocialNLP) 2021 6
Tasks: Stance Classification

On September 2020 a constitutional referendum was held in Italy. In this work we collect a dataset of 1.2M tweets related to this event, with particular interest to the textual content shared, and we design a hashtag-based semi-automatic approach to label them as Supporters or Against the referendum. We use the labelled dataset to train a classifier based on transformers, unsupervisedly pre-trained on Italian corpora. Our model generalizes well on tweets that cannot be labeled by the hashtag-based approach. We check that no length-, lexicon- and sentiment-biases are present to affect the performance of the classifier. Finally, we discuss the discrepancy between the magnitudes of tweets expressing a specific stance, obtained using both the hashtag-based approach and our trained classifier, and the real outcome of the referendum: the referendum was approved by 70% of the voters, while the number of tweets against the referendum is four times greater than the number of tweets supporting it. We conclude that the Italian referendum was an example of event where the minority was very loud on social media, highly influencing the perception of the event. Analyzing only the activity on social media is dangerous and can lead to extremely wrong forecasts.
A Case Study of In-House Competition for Ranking Constructive Comments in a News Service
Authors: Hayato Kobayashi, Hiroaki Taguchi, Yoshimune Tabuchi, Chahine Koleejan, Ken Kobayashi, Soichiro Fujita, Kazuma Murao, Takeshi Masuyama, Taichi Yatsuka, Manabu Okumura, Satoshi Sekine
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SocialNLP) 2021 6

Ranking the user comments posted on a news article is important for online news services because comment visibility directly affects the user experience. Research on ranking comments with different metrics to measure the comment quality has shown “constructiveness” used in argument analysis is promising from a practical standpoint. In this paper, we report a case study in which this constructiveness is examined in the real world. Specifically, we examine an in-house competition to improve the performance of ranking constructive comments and demonstrate the effectiveness of the best obtained model for a commercial service.
Quantifying the Effects of COVID-19 on Restaurant Reviews
Authors: Ivy Cao, Zizhou Liu, Giannis Karamanolakis, Daniel Hsu, Luis Gravano
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SocialNLP) 2021 6
Tasks: Time Series Analysis

The COVID-19 pandemic has implications beyond physical health, affecting society and economies. Government efforts to slow down the spread of the virus have had a severe impact on many businesses, including restaurants. Mandatory policies such as restaurant closures, bans on social gatherings, and social distancing restrictions have affected restaurant operations as well as customer preferences (e.g., prompting a demand of stricter hygiene standards). As of now, however, it is not clear how and to what extent the pandemic has affected restaurant reviews, an analysis of which could potentially inform policies for addressing this ongoing situation. In this work, we present our efforts to understand the effects of COVID-19 on restaurant reviews, with a focus on Yelp reviews produced during the pandemic for New York City and Los Angeles County restaurants. Overall, we make the following contributions. First, we assemble a dataset of 600 reviews with manual annotations of fine-grained COVID-19 aspects related to restaurants (e.g., hygiene practices, service changes, sympathy and support for local businesses). Second, we address COVID-19 aspect detection using supervised classifiers, weakly-supervised approaches based on keywords, and unsupervised topic modeling approaches, and experimentally show that classifiers based on pre-trained BERT representations achieve the best performance (F1=0.79). Third, we analyze the number and evolution of COVID-related aspects over time and show that the resulting time series have substantial correlation (Spearman’s \rho=0.84) with critical statistics related to the COVID-19 pandemic, including the number of new COVID-19 cases. To our knowledge, this is the first work analyzing the effects of COVID-19 on Yelp restaurant reviews and could potentially inform policies by public health departments, for example, to cover resource utilization.
Assessing Cognitive Linguistic Influences in the Assignment of Blame
Authors: Karen Zhou, Ana Smith, Lillian Lee
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SocialNLP) 2021 6

Lab studies in cognition and the psychology of morality have proposed some thematic and linguistic factors that influence moral reasoning. This paper assesses how well the findings of these studies generalize to a large corpus of over 22,000 descriptions of fraught situations posted to a dedicated forum. At this social-media site, users judge whether or not an author is in the wrong with respect to the event that the author described. We find that, consistent with lab studies, there are statistically significant differences in uses of first-person passive voice, as well as first-person agents and patients, between descriptions of situations that receive different blame judgments. These features also aid performance in the task of predicting the eventual collective verdicts.
Reconsidering Annotator Disagreement about Racist Language: Noise or Signal?
Authors: Savannah Larimore, Ian Kennedy, Breon Haskett, Alina Arseniev-Koehler
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SocialNLP) 2021 6
Tasks: Descriptive

An abundance of methodological work aims to detect hateful and racist language in text. However, these tools are hampered by problems like low annotator agreement and remain largely disconnected from theoretical work on race and racism in the social sciences. Using annotations of 5188 tweets from 291 annotators, we investigate how annotator perceptions of racism in tweets vary by annotator racial identity and two text features of the tweets: relevant keywords and latent topics identified through structural topic modeling. We provide a descriptive summary of our data and estimate a series of generalized linear models to determine if annotator racial identity and our 12 latent topics, alone or in combination, explain the way racial sentiment was annotated, net of relevant annotator characteristics and tweet features. Our results show that White and non-White annotators exhibit significant differences in ratings when reading tweets with high prevalence of particular, racially-charged topics. We conclude by suggesting how future methodological work can draw on our results and further incorporate social science theory into analyses.
Understanding and Interpreting the Impact of User Context in Hate Speech Detection
Authors: Edoardo Mosca, Maximilian Wich, Georg Groh
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SocialNLP) 2021 6
Tasks: Hate Speech Detection

As hate speech spreads on social media and online communities, research continues to work on its automatic detection. Recently, recognition performance has been increasing thanks to advances in deep learning and the integration of user features. This work investigates the effects that such features can have on a detection model. Unlike previous research, we show that simple performance comparison does not expose the full impact of including contextual- and user information. By leveraging explainability techniques, we show (1) that user features play a role in the model’s decision and (2) how they affect the feature space learned by the model. Besides revealing that—and also illustrating why—user features are the reason for performance gains, we show how such techniques can be combined to better understand the model and to detect unintended bias.
Self-Contextualized Attention for Abusive Language Identification
Authors: Horacio Jarquín-Vásquez, Hugo Jair Escalante, Manuel Montes
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SocialNLP) 2021 6
Tasks: Abusive Language, Language Identification

The use of attention mechanisms in deep learning approaches has become popular in natural language processing due to its outstanding performance. The use of these mechanisms allows one managing the importance of the elements of a sequence in accordance to their context, however, this importance has been observed independently between the pairs of elements of a sequence (self-attention) and between the application domain of a sequence (contextual attention), leading to the loss of relevant information and limiting the representation of the sequences. To tackle these particular issues we propose the self-contextualized attention mechanism, which trades off the previous limitations, by considering the internal and contextual relationships between the elements of a sequence. The proposed mechanism was evaluated in four standard collections for the abusive language identification task achieving encouraging results. It outperformed the current attention mechanisms and showed a competitive performance with respect to state-of-the-art approaches.
Unsupervised Domain Adaptation in Cross-corpora Abusive Language Detection
Authors: Tulika Bose, Irina Illina, Dominique Fohr
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SocialNLP) 2021 6
Tasks: Abusive Language, Domain Adaptation, Language Modeling, Language Modelling, Unsupervised Domain Adaptation

The state-of-the-art abusive language detection models report great in-corpus performance, but underperform when evaluated on abusive comments that differ from the training scenario. As human annotation involves substantial time and effort, models that can adapt to newly collected comments can prove to be useful. In this paper, we investigate the effectiveness of several Unsupervised Domain Adaptation (UDA) approaches for the task of cross-corpora abusive language detection. In comparison, we adapt a variant of the BERT model, trained on large-scale abusive comments, using Masked Language Model (MLM) fine-tuning. Our evaluation shows that the UDA approaches result in sub-optimal performance, while the MLM fine-tuning does better in the cross-corpora setting. Detailed analysis reveals the limitations of the UDA approaches and emphasizes the need to build efficient adaptation methods for this task.
Jujeop: Korean Puns for K-pop Stars on Social Media
Authors: Soyoung Oh, Jisu Kim, Seungpeel Lee, Eunil Park
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SocialNLP) 2021 6

Jujeop is a type of pun and a unique way for fans to express their love for the K-pop stars they follow using Korean. One of the unique characteristics of Jujeop is its use of exaggerated expressions to compliment K-pop stars, which contain or lead to humor. Based on this characteristic, Jujeop can be separated into four distinct types, with their own lexical collocations: (1) Fragmenting words to create a twist, (2) Homophones and homographs, (3) Repetition, and (4) Nonsense. Thus, the current study first defines the concept of Jujeop in Korean, manually labels 8.6K comments and annotates the comments to one of the four Jujeop types. With the given annotated corpus, this study presents distinctive characteristics of Jujeop comments compared to the other comments by classification task. Moreover, with the clustering approach, we proposed a structural dependency within each Jujeop type. We have made our dataset publicly available for future research of Jujeop expressions.
Identifying Distributional Perspectives from Colingual Groups
Authors: Yufei Tian, Tuhin Chakrabarty, Fred Morstatter, Nanyun Peng
Date: 2021-06-01 00:00:00
Proceeding: NAACL (SocialNLP) 2021 6

Discrepancies exist among different cultures or languages. A lack of mutual understanding among different colingual groups about the perspectives on specific values or events may lead to uninformed decisions or biased opinions. Thus, automatically understanding the group perspectives can provide essential back-ground for many natural language processing tasks. In this paper, we study colingual groups and use language corpora as a proxy to identify their distributional perspectives. We present a novel computational approach to learn shared understandings, and benchmark our method by building culturally-aware models for the English, Chinese, and Japanese languages. Ona held out set of diverse topics, including marriage, corruption, democracy, etc., our model achieves high correlation with human judgements regarding intra-group values and inter-group differences
The Influence of Background Data Size on the Performance of a Score-based Likelihood Ratio System: A Case of Forensic Text Comparison
Authors: Shunichi Ishihara
Date: 2020-12-01 00:00:00
Proceeding: ALTA 2020 12

This study investigates the robustness and stability of a likelihood ratio–based (LR-based) forensic text comparison (FTC) system against the size of background population data. Focus is centred on a score-based approach for estimating authorship LRs. Each document is represented with a bag-of-words model, and the Cosine distance is used as the score-generating function. A set of population data that differed in the number of scores was synthesised 20 times using the Monte-Carol simulation technique. The FTC system’s performance with different population sizes was evaluated by a gradient metric of the log–LR cost (Cllr). The experimental results revealed two outcomes: 1) that the score-based approach is rather robust against a small population size—in that, with the scores obtained from the 40~60 authors in the database, the stability and the performance of the system become fairly comparable to the system with a maximum number of authors (720); and 2) that poor performance in terms of Cllr, which occurred because of limited background population data, is largely due to poor calibration. The results also indicated that the score-based approach is more robust against data scarcity than the feature-based approach; however, this finding obliges further study.
Feature-Based Forensic Text Comparison Using a Poisson Model for Likelihood Ratio Estimation
Authors: Michael Carne, Shunichi Ishihara
Date: 2020-12-01 00:00:00
Proceeding: ALTA 2020 12
Tasks: Authorship Attribution, feature selection

Score- and feature-based methods are the two main ones for estimating a forensic likelihood ratio (LR) quantifying the strength of evidence. In this forensic text comparison (FTC) study, a score-based method using the Cosine distance is compared with a feature-based method built on a Poisson model with texts collected from 2,157 authors. Distance measures (e.g. Burrows’s Delta, Cosine distance) are a standard tool in authorship attribution studies. Thus, the implementation of a score-based method using a distance measure is naturally the first step for estimating LRs for textual evidence. However, textual data often violates the statistical assumptions underlying distance-based models. Furthermore, such models only assess the similarity, not the typicality, of the objects (i.e. documents) under comparison. A Poisson model is theoretically more appropriate than distance-based measures for authorship attribution, but it has never been tested with linguistic text evidence within the LR framework. The log-LR cost (Cllr) was used to assess the performance of the two methods. This study demonstrates that: (1) the feature-based method outperforms the score-based method by a Cllr value of ca. 0.09 under the best-performing settings and; (2) the performance of the feature-based method can be further improved by feature selection.