Canada

It is common sense that one should prefer to eat a salad with a fork rather than with a chainsaw. However, for eating a bowl of rice, the choice between a fork and a pair of chopsticks is culturally relative. We introduce FORK, a small, manually-curated set of CommonsenseQA-style questions for probing cultural biases and assumptions present in commonsense reasoning systems, with a specific focus on food-related customs. We test several CommonsenseQA systems on FORK, and while we see high performance on questions about the US culture, the poor performance of these systems on questions about non-US cultures highlights systematic cultural assumptions aligned with US over non-US cultures.

ACL 2023

FORK: A Bite-Sized Test Set for Probing Culinary Cultural Biases in Commonsense Reasoning Models

cultural bias

commonsenseqa

bias and fairness

commonsense reasoning

natural language processing

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
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The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

workshop paper

Rumour detection on social media is an important topic due to the challenges of misinformation propagation and slow verification of misleading information. Most previous work focus on the response posts on social media, ignoring the useful characteristics of involved users and their relations. In this paper, we propose a novel framework, Post-User Fusion Network (PESTO), which models the patterns of rumours from both post diffusion and user social networks. Specifically, we propose a novel Chronologically-masked Transformer architecture to model both temporal sequence and diffusion structure of rumours, and apply a Relational Graph Convolutional Network to model the social relations of involved users, with a fusion network based on self-attention mechanism to incorporate the two aspects. Additionally, two data augmentation techniques are leveraged to improve the robustness and accuracy of our models. Empirical results on four datasets of English tweets show the superiority of the proposed method.

PESTO: A Post-User Fusion Network for Rumour Detection on Social Media

The task of predicting reader appreciation or literary quality has been the object of several studies. It remains, however, a challenging problem in quantitative literary analyses and computational linguistics alike, as its definition can vary a lot depending on the genre of liter- ary texts considered, the features adopted, and the annotation system employed. This paper attempts to evaluate the impact on reader ap- preciation, defined as online users’ ratings, of sentiment range and sentiment arc patterns ver- sus traditional stylometric features. We run our experiments on a corpus of English-language literary fiction, showing that stylometric fea- tures alone are helpful in modelling literary quality, but can be outperformed by analysing the novels’ sentimental profile.

Sentimental Matters - Predicting Literary Quality by Sentiment Analysis and Stylometric Features

Aspect-based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task which involves four elements from user-generated texts: aspect term, aspect category, opinion term, and sentiment polarity. Most computational approaches focus on some of the ABSA sub-tasks such as tuple (aspect term, sentiment polarity) or triplet (aspect term, opinion term, sentiment polarity) extraction using either pipeline or joint modeling approaches. Recently, generative approaches have been proposed to extract all four elements as (one or more) quadruplets from text as a single task. In this work, we take a step further and propose a unified framework for solving ABSA, and the associated sub-tasks to improve the performance in few-shot scenarios. To this end, we fine-tune a T5 model with instructional prompts in a multi-task learning fashion covering all the sub-tasks, as well as the entire quadruple prediction task. In experiments with multiple benchmark datasets, we show that the proposed multi-task prompting approach brings performance boost (by absolute 8.29 F1) in the few-shot learning setting.

Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis

In this work we use consumed text to infer Big-5 personality inventories using data we have collected from the social media platform Reddit. We test our models on two datasets, sampled from participants who consumed either fiction content (N = 913) or news content (N = 213). We show that state-of-the-art models from a similar task using authored text do not translate well to this task, with average correlations of r=.06 between the model's predictions and ground-truth personality inventory dimensions. We propose an alternate method of generating average personality labels for each piece of text consumed, under which our model achieves correlations as high as r=.34 when predicting personality from the text being read.

You Are What You Read: Inferring Personality From Consumed Textual Content

Verbal deception has been studied in psychology, forensics, and computational linguistics for a variety of reasons, like understanding behaviour patterns, identifying false testimonies, and detecting deception in online communication. Varying motivations across research fields lead to differences in the domain choices to study and in the conceptualization of deception, making it hard to compare models and build robust deception detection systems for a given language. With this paper, we improve this situation by surveying available English deception datasets which include domains like social media reviews, court testimonials, opinion statements on specific topics, and deceptive dialogues from online strategy games. We consolidate these datasets into a single unified corpus. Based on this resource, we conduct a correlation analysis of linguistic cues of deception across datasets to understand the differences and perform cross-corpus modeling experiments which show that a cross domain generalization is challenging to achieve.

UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception Detection

The scarcity of annotated data is a major impediment to natural language processing (NLP) research in Bengali, a language that is considered low-resource. In particular, the health and medical domains suffer from a severe paucity of annotated data. Thus, this study aims to introduce BanglaSocialHealth, an annotated social media health corpus that provides sentence-level annotations of four distinct types of expression modes, namely narrative (NAR), informative (INF), suggestive (SUG), and inquiring (INQ) modes in Bengali. We provide details regarding the annotation procedures and report various statistics, such as the median and mean length of words in different sentence modes. Additionally, we apply classical machine learning (CML) classifiers and transformer-based language models to classify sentence modes. We find that most of the statistical properties are similar in different types of sentence modes. To determine the sentence mode, the transformer-based M-BERT model provides slightly better efficacy than the CML classifiers. Our developed corpus and analysis represent a much-needed contribution to Bengali NLP research in medical and health domains and have the potential to facilitate a range of downstream tasks, including question-answering, misinformation detection, and information retrieval.

Discourse Mode Categorization of Bengali Social Media Health Text

Paraphrase generation, a.k.a. paraphrasing, is a common and important task in natural language processing. Emotional paraphrasing, which changes the emotion embodied in a piece of text while preserving its meaning, has many potential applications, including moderating online dialogues and preventing cyberbullying. We introduce a new task of fine-grained emotional paraphrasing along emotion gradients, that is, altering the emotional intensities of the paraphrases in fine-grained settings following smooth variations in affective dimensions while preserving the meaning of the original text. We reconstruct several widely used paraphrasing datasets by augmenting the input and target texts with their fine-grained emotion labels. Then, we propose a framework for emotion and sentiment guided paraphrasing by leveraging pre-trained language models for conditioned text generation. Extensive evaluation of the fine-tuned models suggests that including fine-grained emotion labels in the paraphrase task significantly improves the likelihood of obtaining high-quality paraphrases that reflect the desired emotions while achieving consistently better scores in paraphrase metrics such as BLEU, ROUGE, and METEOR.

Emotion and Sentiment Guided Paraphrasing

Work on emotion detection is often focused on textual data from i.e. Social Media. If multimodal data (i.e. speech) is analysed, the focus again is often placed on the transcription. 
This paper takes a closer look at how crucial acoustic information actually is for the recognition of emotions from multimodal data. To this end we use the IEMOCAP data, which is one of the larger data sets that provides transcriptions, audio recordings and manual emotion categorization. We build models for emotion classification using text-only, acoustics-only and combining both modalities in order to examine the influence of the various modalities on the final categorization. Our results indicate that using text-only models outperform acoustics-only models. But combining text-only and acoustic-only models improves the results. 
Additionally, we perform a qualitative analysis and find that a range of misclassifications are due to factors not related to the model, but to the data such as, recording quality, a challenging classification task and misclassifications that are unsurprising for humans.

Emotions in Spoken Language - Do we need acoustics?

The valence analysis of speakers' utterances or written posts helps to understand the activation and variations of the emotional state throughout the conversation. More recently, the concept of Emotion Carriers (EC) has been introduced to explain the emotion felt by the speaker and its manifestations. In this work, we investigate the natural inter-dependency of valence and ECs via a multi-task learning approach. We experiment with Pre-trained Language Models (PLM) for single-task, two-step, and joint settings for the valence and EC prediction tasks. We compare and evaluate the performance of generative (GPT-2) and discriminative (BERT) architectures in each setting. We observed that providing the ground truth label of one task improves the prediction performance of the models in the other task. We further observed that the discriminative model achieves the best trade-off of valence and EC prediction tasks in the joint prediction setting. As a result, we attain a single model that performs both tasks, thus, saving computation resources at training and inference times.

Understanding Emotion Valence is a Joint Deep Learning Task

We present the Verifee dataset: a multimodal dataset of news articles with fine-grained trustworthiness annotations. We bring a diverse set of researchers from social, media, and computer sciences aboard to study this interdisciplinary problem holistically and develop a detailed methodology that assesses the texts through the lens of editorial transparency, journalist conventions, and objective reporting while penalizing manipulative techniques. We collect over 10,000 annotated articles from 60 Czech online news sources. Each item is categorized into one of the 4 proposed classes on the credibility spectrum -- ranging from entirely trustworthy articles to deceptive ones -- and annotated of its manipulative attributes. We fine-tune prominent sequence-to-sequence language models for the trustworthiness classification task on our dataset and report the best F-1 score of 0.53. We open-source the dataset, annotation methodology, and annotators' instructions in full length at https://www.verifee.ai/research/ to enable easy build-up work.

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PESTO: A Post-User Fusion Network for Rumour Detection on Social Media

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FORK: A Bite-Sized Test Set for Probing Culinary Cultural Biases in Commonsense Reasoning Models