ymoslem/acl-abstracts huggingface dataset:
Automating Behavioral Testing in Machine Translation (2023)

Behavioral testing in NLP allows fine-grained evaluation of systems by examining their linguistic capabilities through the analysis of input-output behavior. Unfortunately, existing work on behavioral testing in Machine Translation (MT) is currently restricted to largely handcrafted tests covering a limited range of capabilities and languages. To address this limitation, we propose to use Large Language Models (LLMs) to generate a diverse set of source sentences tailored to test the behavior of MT models in a range of situations. We can then verify whether the MT model exhibits the expected behavior through matching candidate sets that are also generated using LLMs. Our approach aims to make behavioral testing of MT systems practical while requiring only minimal human effort. In our experiments, we apply our proposed evaluation framework to assess multiple available MT systems, revealing that while in general pass-rates follow the trends observable from traditional accuracy-based metrics, our method was able to uncover several important differences and potential bugs that go unnoticed when relying only on accuracy.
One Wide Feedforward Is All You Need (2023)

The Transformer architecture has two main non-embedding components: Attention and the Feed Forward Network (FFN). Attention captures interdependencies between words regardless of their position, while the FFN non-linearly transforms each input token independently. In this work we explore the role of the FFN, and find that despite taking up a significant fraction of the model`s parameters, it is highly redundant. Concretely, we are able to substantially reduce the number of parameters with only a modest drop in accuracy by removing the FFN on the decoder layers and sharing a single FFN across the encoder. Finally we scale this architecture back to its original size by increasing the hidden dimension of the shared FFN, achieving substantial gains in both accuracy and latency with respect to the original Transformer Big.
A Benchmark for Evaluating Machine Translation Metrics on Dialects without Standard Orthography (2023)

For sensible progress in natural language processing, it is important that we are aware of the limitations of the evaluation metrics we use. In this work, we evaluate how robust metrics are to non-standardized dialects, i.e. spelling differences in language varieties that do not have a standard orthography. To investigate this, we collect a dataset of human translations and human judgments for automatic machine translations from English to two Swiss German dialects. We further create a challenge set for dialect variation and benchmark existing metrics' performances. Our results show that existing metrics cannot reliably evaluate Swiss German text generation outputs, especially on segment level. We propose initial design adaptations that increase robustness in the face of non-standardized dialects, although there remains much room for further improvement. The dataset, code, and models are available here: https://github.com/textshuttle/dialect{\_}eval
The Devil Is in the Errors: Leveraging Large Language Models for Fine-grained Machine Translation Evaluation (2023)

Automatic evaluation of machine translation (MT) is a critical tool driving the rapid iterative development of MT systems. While considerable progress has been made on estimating a single scalar quality score, current metrics lack the informativeness of more detailed schemes that annotate individual errors, such as Multidimensional Quality Metrics (MQM). In this paper, we help fill this gap by proposing AutoMQM, a prompting technique which leverages the reasoning and in-context learning capabilities of large language models (LLMs) and asks them to identify and categorize errors in translations. We start by evaluating recent LLMs, such as PaLM and PaLM-2, through simple score prediction prompting, and we study the impact of labeled data through in-context learning and finetuning. We then evaluate AutoMQM with PaLM-2 models, and we find that it improves performance compared to just prompting for scores (with particularly large gains for larger models) while providing interpretability through error spans that align with human annotations.
Overview of the 10th Workshop on Asian Translation (2023)

This paper presents the results of the shared tasks from the 10th workshop on Asian translation (WAT2023). For the WAT2023, 2 teams submitted their translation results for the human evaluation. We also accepted 1 research paper. About 40 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.
Mitigating Domain Mismatch in Machine Translation via Paraphrasing (2023)

Quality of machine translation (MT) deteriorates significantly when translating texts having characteristics that differ from the training data, such as content domain. Although previous studies have focused on adapting MT models on a bilingual parallel corpus in the target domain, this approach is not applicable when no parallel data are available for the target domain or when utilizing black-box MT systems. To mitigate problems caused by such domain mismatch without relying on any corpus in the target domain, this study proposes a method to search for better translations by paraphrasing input texts of MT. To obtain better translations even for input texts from unforeknown domains, we generate their multiple paraphrases, translate each, and rerank the resulting translations to select the most likely one. Experimental results on Japanese-to-English translation reveal that the proposed method improves translation quality in terms of BLEU score for input texts from specific domains.
BITS-P at WAT 2023: Improving Indic Language Multimodal Translation by Image Augmentation using Diffusion Models (2023)

This paper describes the proposed system for mutlimodal machine translation. We have participated in multimodal translation tasks for English into three Indic languages: Hindi, Bengali, and Malayalam. We leverage the inherent richness of multimodal data to bridge the gap of ambiguity in translation. We fine-tuned the {\textquoteleft}No Language Left Behind' (NLLB) machine translation model for multimodal translation, further enhancing the model accuracy by image data augmentation using latent diffusion. Our submission achieves the best BLEU score for English-Hindi, English-Bengali, and English-Malayalam language pairs for both Evaluation and Challenge test sets.
OdiaGenAI`s Participation at WAT2023 (2023)

This paper offers an in-depth overview of the team {\textquotedblleft}ODIAGEN`s{\textquotedblright} translation system submitted to the Workshop on Asian Translation (WAT2023). Our focus lies in the domain of Indic Multimodal tasks, specifically targeting English to Hindi, English to Malayalam, and English to Bengali translations. The system uses a state-of-the-art Transformer-based architecture, specifically the NLLB-200 model, fine-tuned with language-specific Visual Genome Datasets. With this robust system, we were able to manage both text-to-text and multimodal translations, demonstrating versatility in handling different translation modes. Our results showcase strong performance across the board, with particularly promising results in the Hindi and Bengali translation tasks. A noteworthy achievement of our system lies in its stellar performance across all text-to-text translation tasks. In the categories of English to Hindi, English to Bengali, and English to Malayalam translations, our system claimed the top positions for both the evaluation and challenge sets. This system not only advances our understanding of the challenges and nuances of Indic language translation but also opens avenues for future research to enhance translation accuracy and performance.
PESTO: A Post-User Fusion Network for Rumour Detection on Social Media (2023)

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.
Sentimental Matters - Predicting Literary Quality by Sentiment Analysis and Stylometric Features (2023)

Over the years, the task of predicting reader appreciation or literary quality has been the object of several studies, but it remains a challenging problem in quantitative literary studies and computational linguistics alike, as its definition can vary a lot depending on the genre, the adopted features and the annotation system. This paper attempts to evaluate the impact of sentiment arc modelling versus more classical stylometric features for user-ratings of novels. We run our experiments on a corpus of English language narrative literary fiction from the 19th and 20th century, showing that syntactic and surface-level features can be powerful for the study of literary quality, but can be outperformed by sentiment-characteristics of a text.
Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis (2023)

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-taskssuch 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) quadrupletsfrom 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-tasksto 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.
You Are What You Read: Inferring Personality From Consumed Textual Content (2023)

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 model 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.
UNIDECOR: A Unified Deception Corpus for Cross-Corpus Deception Detection (2023)

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. The unified deception corpus (UNIDECOR) can be obtained from \url{https://www.ims.uni-stuttgart.de/data/unidecor}.
Discourse Mode Categorization of Bengali Social Media Health Text (2023)

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.
Emotion and Sentiment Guided Paraphrasing (2023)

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.
Emotions in Spoken Language - Do we need acoustics? (2023)

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.
Understanding Emotion Valence is a Joint Deep Learning Task (2023)

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.
Czech-ing the News: Article Trustworthiness Dataset for Czech (2023)

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 \url{https://www.verifee.ai/research/} to enable easy build-up work.
Towards Detecting Harmful Agendas in News Articles (2023)

Manipulated news online is a growing problem which necessitates the use of automated systems to curtail its spread. We argue that while misinformation and disinformation detection have been studied, there has been a lack of investment in the important open challenge of detecting harmful agendas in news articles; identifying harmful agendas is critical to flag news campaigns with the greatest potential for real world harm. Moreover, due to real concerns around censorship, harmful agenda detectors must be interpretable to be effective. In this work, we propose this new task and release a dataset, NewsAgendas, of annotated news articles for agenda identification. We show how interpretable systems can be effective on this task and demonstrate that they can perform comparably to black-box models.
GSAC: A Gujarati Sentiment Analysis Corpus from Twitter (2023)

Sentiment Analysis is an important task for analysing online content across languages for tasks such as content moderation and opinion mining. Though a significant amount of resources are available for Sentiment Analysis in several Indian languages, there do not exist any large-scale, open-access corpora for Gujarati. Our paper presents and describes the Gujarati Sentiment Analysis Corpus (GSAC), which has been sourced from Twitter and manually annotated by native speakers of the language. We describe in detail our collection and annotation processes and conduct extensive experiments on our corpus to provide reliable baselines for future work using our dataset.
A Dataset for Explainable Sentiment Analysis in the German Automotive Industry (2023)

While deep learning models have greatly improved the performance of many tasks related to sentiment analysis and classification, they are often criticized for being untrustworthy due to their black-box nature. As a result, numerous explainability techniques have been proposed to better understand the model predictions and to improve the deep learning models. In this work, we introduce InfoBarometer, the first benchmark for examining interpretable methods related to sentiment analysis in the German automotive sector based on online news. Each news article in our dataset is annotated w.r.t. overall sentiment (i.e., positive, negative and neutral), the target of the sentiment (focusing on innovation-related topics such as e.g. electromobility) and the rationales, i.e., textual explanations for the sentiment label that can be leveraged during both training and evaluation. For this research, we compare different state-of-the-art approaches to perform sentiment analysis and observe that even models that perform very well in classification do not score high on explainability metrics like model plausibility and faithfulness. We calculated the polarity scores for the best method BERT and got an F-score of 73.6. Moreover, we evaluated different interpretability algorithms (LIME, SHAP, Integrated Gradients, Saliency) based on explicitly marked rationales by human annotators quantitatively and qualitatively. Our experiments demonstrate that the textual explanations often do not agree with human interpretations, and rarely help to justify the models decision. However, local and global features provide useful insights to help uncover spurious features in the model and biases within the dataset. We intend to make our dataset public for other researchers
Examining Bias in Opinion Summarisation through the Perspective of Opinion Diversity (2023)

Opinion summarisation is a task that aims to condense the information presented in the source documents while retaining the core message and opinions. A summary that only represents the majority opinions will leave the minority opinions unrepresented in the summary. In this paper, we use the stance towards a certain target as an opinion. We study bias in opinion summarisation from the perspective of opinion diversity, which measures whether the model generated summary can cover a diverse set of opinions. In addition, we examine opinion similarity, a measure of how closely related two opinions are in terms of their stance on a given topic, and its relationship with opinion diversity. Through the lense of stances towards a topic, we examine opinion diversity and similarity using three debatable topics under COVID-19. Experimental results on these topics revealed that a higher degree of similarity of opinions did not indicate good diversity or fairly cover the various opinions originally presented in the source documents. We found that BART and ChatGPT can better capture diverse opinions presented in the source documents.