ymoslem/acl-abstracts huggingface dataset:
LitPC: A set of tools for building parallel corporafrom literary works (2024)

In this paper, we describe the LitPC toolkit, a variety of tools and methods designed for the quick and effective creation of parallel corpora derived from literary works. This toolkit can be a useful resource due to the scarcity of curated parallel texts for this domain. We also feature a case study describing the creation of a Russian-English parallel corpus based on the literary works by Leo Tolstoy. Furthermore, an augmented version of this corpus is used to both train and assess neural machine translation systems specifically adapted to the author`s style.
Prompting Large Language Models for Idiomatic Translation (2024)

Large Language Models (LLMs) have demonstrated impressive performance in translating content across different languages and genres. Yet, their potential in the creative aspects of machine translation has not been fully explored. In this paper, we seek to identify the strengths and weaknesses inherent in different LLMs when applied to one of the most prominent features of creative works: the translation of idiomatic expressions. We present an overview of their performance in the EN$\rightarrow$IT language pair, a context characterized by an evident lack of bilingual data tailored for idiomatic translation. Lastly, we investigate the impact of prompt design on the quality of machine translation, drawing on recent findings which indicate a substantial variation in the performance of LLMs depending on the prompts utilized.
An Analysis of Surprisal Uniformity in Machine and Human Translations (2024)

This study examines neural machine translation (NMT) and its performance on texts that diverege from typical standards, focusing on how information is organized within sentences. We analyze surprisal distributions in source texts, human translations, and machine translations across several datasets to determine if NMT systems naturally promote a uniform density of surprisal in their translations, even when the original texts do not adhere to this principle.The findings reveal that NMT tends to align more closely with source texts in terms of surprisal uniformity compared to human translations.We analyzed absolute values of the surprisal uniformity measures as well, expecting that human translations will be less uniform. In contradiction to our initial hypothesis, we did not find comprehensive evidence for this claim, with some results suggesting this might be the case for very diverse texts, like poetry.
Impact of translation workflows with and without MT on textual characteristics in literary translation (2024)

The use of machine translation is increasingly being explored for the translation of literary texts, but there is still a lot of uncertainty about the optimal translation workflow in these scenarios. While overall quality is quite good, certain textual characteristics can be different in a human translated text and a text produced by means of machine translation post-editing, which has been shown to potentially have an impact on reader perceptions and experience as well. In this study, we look at textual characteristics from short story translations from B.J. Novak`s One more thing into Dutch. Twenty-three professional literary translators translated three short stories, in three different conditions: using Word, using the classic CAT tool Trados, and using a machine translation post-editing platform specifically designed for literary translation. We look at overall text characteristics (sentence length, type-token ratio, stylistic differences) to establish whether translation workflow has an impact on these features, and whether the three workflows lead to very different final translations or not.
Machine Translation Meets Large Language Models: Evaluating ChatGPT`s Ability to Automatically Post-Edit Literary Texts (2024)

Large language models such as GPT-4 have been trained on vast corpora, giving them excellent language understanding. This study explores the use of ChatGPT for post-editing machine translations of literary texts. Three short stories, machine translated from English into Dutch, were post-edited by 7-8 professional translators and ChatGPT. Automatic metrics were used to evaluate the number and type of edits made, and semantic and syntactic similarity between the machine translation and the corresponding post-edited versions. A manual analysis classified errors in the machine translation and changes made by the post-editors. The results show that ChatGPT made more changes than the average post-editor. ChatGPT improved lexical richness over machine translation for all texts. The analysis of editing types showed that ChatGPT replaced more words with synonyms, corrected fewer machine errors and introduced more problems than professionals.
Detecting Calls to Action in Multimodal Content: Analysis of the 2021 German Federal Election Campaign on Instagram (2024)

This study investigates the automated classification of Calls to Action (CTAs) within the 2021 German Instagram election campaign to advance the understanding of mobilization in social media contexts. We analyzed over 2,208 Instagram stories and 712 posts using fine-tuned BERT models and OpenAI`s GPT-4 models. The fine-tuned BERT model incorporating synthetic training data achieved a macro F1 score of 0.93, demonstrating a robust classification performance. Our analysis revealed that 49.58{\%} of Instagram posts and 10.64{\%} of stories contained CTAs, highlighting significant differences in mobilization strategies between these content types. Additionally, we found that FDP and the Greens had the highest prevalence of CTAs in posts, whereas CDU and CSU led in story CTAs.
Multilingual Bot Accusations: How Different Linguistic Contexts Shape Perceptions of Social Bots (2024)

Recent research indicates that the online use of the term {\textquotedblright}bot{\textquotedblright} has evolved over time. In the past, people used the term to accuse others of displaying automated behavior. However, it has gradually transformed into a linguistic tool to dehumanize the conversation partner, particularly on polarizing topics. Although this trend has been observed in English-speaking contexts, it is still unclear whether it holds true in other socio-linguistic environments. In this work we extend existing work on bot accusations and explore the phenomenon in a multilingual setting. We identify three distinct accusation patterns that characterize the different languages.
Operationalising the Hermeneutic Grouping Process in Corpus-assisted Discourse Studies (2024)

We propose a framework for quantitative-qualitative research in corpus-assisted discourse studies (CADS), which operationalises the central process of manually forming groups of related words and phrases in terms of {\textquotedblleft}discoursemes{\textquotedblright} and their constellations. We introduce an open-source implementation of this framework in the form of a REST API based on Corpus Workbench. Going through the workflow of a collocation analysis for fleeing and related terms in the German Federal Parliament, the paper gives details about the underlying algorithms, with available parameters and further possible choices. We also address multi-word units (which are often disregarded by CADS tools), a semantic map visualisation of collocations, and how to compute assocations between discoursemes.
A Few Hypocrites: Few-Shot Learning and Subtype Definitions for Detecting Hypocrisy Accusations in Online Climate Change Debates (2024)

The climate crisis is a salient issue in online discussions, and hypocrisy accusations are a central rhetorical element in these debates. However, for large-scale text analysis, hypocrisy accusation detection is an understudied tool, most often defined as a smaller subtask of fallacious argument detection. In this paper, we define hypocrisy accusation detection as an independent task in NLP, and identify different relevant subtypes of hypocrisy accusations. Our Climate Hypocrisy Accusation Corpus (CHAC) consists of 420 Reddit climate debate comments, expert-annotated into two different types of hypocrisy accusations: personal versus political hypocrisy. We evaluate few-shot in-context learning with 6 shots and 3 instruction-tuned Large Language Models (LLMs) for detecting hypocrisy accusations in this dataset. Results indicate that the GPT-4o and Llama-3 models in particular show promise in detecting hypocrisy accusations (F1 reaching 0.68, while previous work shows F1 of 0.44). However, context matters for a complex semantic concept such as hypocrisy accusations, and we find models struggle especially at identifying political hypocrisy accusations compared to personal moral hypocrisy. Our study contributes new insights in hypocrisy detection and climate change discourse, and is a stepping stone for large-scale analysis of hypocrisy accusation in online climate debates.
Language Complexity in Populist Rhetoric (2024)

Research suggests that politicians labeled as populists tend to use simpler language than their mainstream opponents. Yet, the metrics traditionally employed to assess the complexity of their language do not show consistent and generalizable results across different datasets and languages. This inconsistencies raise questions about the claimed simplicity of populist discourse, suggesting that the issue may be more nuanced than it initially seemed. To address this topic, we analyze the linguistic profile of IMPAQTS, a dataset of transcribed Italian political speeches, to identify linguistic features differentiating populist and non-populist parties. Our methodology ensures comparability of political texts and combines various statistical analyses to reliably identify key linguistic characteristics to test our case study. Results show that the {\textquotedblleft}simplistic{\textquotedblright} language features previously described in the literature are not robust predictors of populism. This suggests that the characteristics defining populist statements are highly dependent on the specific dataset and the language being analysed, thus limiting the conclusions drawn in previous research. In our study, various linguistic features statistically differentiate between populist and mainstream parties, indicating that populists tend to employ specific well-known rhetorical strategies more frequently; however, none of them strongly indicate that populist parties use simpler language.
ChatGPT as Your n-th Annotator: Experiments in Leveraging Large Language Models for Social Science Text Annotation in Slovak Language (2024)

Large Language Models (LLMs) are increasingly influential in Computational Social Science, offering new methods for processing and analyzing data, particularly in lower-resource language contexts. This study explores the use of OpenAI`s GPT-3.5 Turbo and GPT-4 for automating annotations for a unique news media dataset in a lower resourced language, focusing on stance classification tasks. Our results reveal that prompting in the native language, explanation generation, and advanced prompting strategies like Retrieval Augmented Generation and Chain of Thought prompting enhance LLM performance, particularly noting GPT-4`s superiority in predicting stance. Further evaluation indicates that LLMs can serve as a useful tool for social science text annotation in lower resourced languages, notably in identifying inconsistencies in annotation guidelines and annotated datasets.
Detecting emotional polarity in Finnish parliamentary proceedings (2024)

Few studies have focused on detecting emotion in parliamentary corpora, and none have done this for the Finnish parliament. In this paper, this gap is addressed by applying the polarity lexicon{--}based methodology of a study by Rheault et al. (2016) on speeches in the British Parliament to a Finnish corpus. The findings show an increase in positive sentiment over time. Additionally, the findings indicate that politicians' emotional states may be impacted by the state of the economy and other major events, such as the Covid-19 pandemic and the Russian invasion of Ukraine.
Topic-specific social science theory in stance detection: a proposal and interdisciplinary pilot study on sustainability initiatives (2024)

Topic-specificity is often seen as a limitation of stance detection models and datasets, especially for analyzing political and societal debates. However, stances contain topic-specific aspects that are crucial for an in-depth understanding of these debates. Our interdisciplinary approach identifies social science theories on specific debate topics as an opportunity for further defining stance detection research and analyzing online debate. This paper explores sustainability as debate topic, and connects stance to the sustainability-related Value-Belief-Norm (VBN) theory. VBN theory states that arguments in favor or against sustainability initiatives contain the dimensions of feeling power to change the issue with the initiative, and thinking whether or not the initiative tackles an urgent threat to the environment. In a pilot study with our Reddit European Sustainability Initiatives corpus, we develop an annotation procedure for these complex concepts. We then compare crowd-workers with Natural Language Processing experts' annotation proficiency. Both crowd-workers and NLP experts find the tasks difficult, but experts reach more agreement on some difficult examples. This pilot study shows that complex theories about debate topics are feasible and worthwhile as annotation tasks for stance detection.
The Echoes of the \textquoteleftI': Tracing Identity with Demographically Enhanced Word Embeddings (2024)

Identity is one of the most commonly studied constructs in social science. However, despite extensive theoretical work on identity, there remains a need for additional empirical data to validate and refine existing theories. This paper introduces a novel approach to studying identity by enhancing word embeddings with socio-demographic information. As a proof of concept, we demonstrate that our approach successfully reproduces and extends established findings regarding gendered self-views. Our methodology can be applied in a wide variety of settings, allowing researchers to tap into a vast pool of naturally occurring data, such as social media posts. Unlike similar methods already introduced in computer science, our approach allows for the study of differences between social groups. This could be particularly appealing to social scientists and may encourage the faster adoption of computational methods in the field.
TPPMI - a Temporal Positive Pointwise Mutual Information Embedding of Words (2024)

We present Temporal Positive Pointwise Mutual Information (TPPMI) embeddings as a robust and data-efficient alternative for modeling temporal semantic change. Based on the assumption that the semantics of the most frequent words in a corpus are relatively stable over time, our model represents words as vectors of their PPMI similarities with a predefined set of such context words. We evaluate our method on the temporal word analogy benchmark of Yao et al. (2018) and compare it to the TWEC model (Di Carlo et al., 2019), demonstrating the competitiveness of the approach. While the performance of TPPMI stays below that of the state-of-the-art TWEC model, it offers a higher degree of interpretability and is applicable in scenarios where only a limited amount of data is available.
Augmented Political Leaning Detection: Leveraging Parliamentary Speeches for Classifying News Articles (2024)

In an era where political discourse infiltrates online platforms and news media, identifying opinion is increasingly critical, especially in news articles, where objectivity is expected. Readers frequently encounter authors' inherent political viewpoints, challenging them to discern facts from opinions. Classifying text on a spectrum from left to right is a key task for uncovering these viewpoints. Previous approaches rely on outdated datasets to classify current articles, neglecting that political opinions on certain subjects change over time. This paper explores a novel methodology for detecting political leaning in news articles by augmenting them with political speeches specific to the topic and publication time. We evaluated the impact of the augmentation using BERT and Mistral models. The results show that the BERT model`s F1 score improved from a baseline of 0.82 to 0.85, while the Mistral model`s F1 score increased from 0.30 to 0.31.
Words That Stick: Using Keyword Cohesion to Improve Text Segmentation (2024)

Text Segmentation (TS) is the idea of segmenting bodies of text into coherent blocks, mostly defined by the topics each segment contains. Historically, techniques in this area have been unsupervised, with more success recently coming from supervised methods instead. Although these approaches see better performance, they require training data and upfront training time. We propose a new method called Coherence, where we use strong sentence embeddings to pull representational keywords as the main constructor of sentences when comparing them to one another. Additionally, we include a storage of previously found keywords for the purposes of creating a more accurate segment representation instead of just the immediate sentence in question. With our system, we show improved results over current state-of-the-art unsupervised techniques when analyzed using Pk and WindowDiff scores. Because its unsupervised, Coherence requires no fine-tuning.
Investigating large language models for their competence in extracting grammatically sound sentences from transcribed noisy utterances (2024)

Selectively processing noisy utterances while effectively disregarding speech-specific elements poses no considerable challenge for humans, as they exhibit remarkable cognitive abilities to separate semantically significant content from speech-specific noise (i.e. filled pauses, disfluencies, and restarts). These abilities may be driven by mechanisms based on acquired grammatical rules that compose abstract syntactic-semantic structures within utterances. Segments without syntactic and semantic significance are consistently disregarded in these structures. The structures, in tandem with lexis, likely underpin language comprehension and thus facilitate effective communication.In our study, grounded in linguistically motivated experiments, we investigate whether large language models (LLMs) can effectively perform analogical speech comprehension tasks. In particular, we examine the ability of LLMs to extract well-structured utterances from transcriptions of noisy dialogues. We conduct two evaluation experiments in the Polish language scenario, using a dataset presumably unfamiliar to LLMs to mitigate the risk of data contamination. Our results show that not all extracted utterances are correctly structured, indicating that either LLMs do not fully acquire syntactic-semantic rules or they acquire them but cannot apply them effectively. We conclude that the ability of LLMs to comprehend noisy utterances is still relatively superficial compared to human proficiency in processing them.
Multi-Cultural Norm Base: Frame-based Norm Discovery in Multi-Cultural Settings (2024)

Sociocultural norms serve as guiding principles for personal conduct in social interactions within a particular society or culture. The study of norm discovery has seen significant development over the last few years, with various interesting approaches. However, it is difficult to adopt these approaches to discover norms in a new culture, as they rely either on human annotations or real-world dialogue contents. This paper presents a robust automatic norm discovery pipeline, which utilizes the cultural knowledge of GPT-3.5 Turbo (ChatGPT) along with several social factors. By using these social factors and ChatGPT, our pipeline avoids the use of human dialogues that tend to be limited to specific scenarios, as well as the use of human annotations that make it difficult and costly to enlarge the dataset. The resulting database - Multi-cultural Norm Base (MNB) - covers 6 distinct cultures, with over 150k sociocultural norm statements in total. A state-of-the-art Large Language Model (LLM), Llama 3, fine-tuned with our proposed dataset, shows remarkable results on various downstream tasks, outperforming models fine-tuned on other datasets significantly.
Lossy Context Surprisal Predicts Task-Dependent Patterns in Relative Clause Processing (2024)

English relative clauses are a critical test case for theories of syntactic processing. Expectation- and memory-based accounts make opposing predictions, and behavioral experiments have found mixed results. We present a technical extension of Lossy Context Surprisal (LCS) and use it to model relative clause processing in three behavioral experiments. LCS predicts key results at distinct retention rates, showing that task-dependent memory demands can account for discrepant behavioral patterns in the literature.
Global-Pruner: A Stable and Efficient Pruner for Retraining-Free Pruning of Encoder-Based Language Models (2024)

Large language models (LLMs) have achieved significant success in complex tasks across various domains, but they come with high computational costs and inference latency issues. Pruning, as an effective method, can significantly reduce inference costs. However, current pruning algorithms for encoder-based language models often focus on locally optimal solutions, neglecting a comprehensive exploration of the global solution space. This oversight can lead to instability in the solution process, thereby affecting the overall performance of the model. To address these challenges, we propose a structured pruning algorithm named G-Pruner (Global Pruner), comprising two integral components: PPOM (Proximal Policy Optimization Mask) and CG{\texttwosuperior}MT (Conjugate Gradient Squared Mask Tuning), utilizing a global optimization strategy. This strategy not only eliminates the need for retraining but also ensures the algorithm`s stability and adaptability to environmental changes, effectively addressing the issue of focusing solely on immediate optima while neglecting long-term effects. This method is evaluated on the GLUE and SQuAD benchmarks using BERTBASE and DistilBERT models. The experimental results indicate that without any retraining, G-Pruner achieves significant accuracy improvements on the SQuAD$_{2.0}$ task with a FLOPs constraint of 60{\%}, demonstrating a 6.02{\%} increase in F1 score compared with baseline algorithms.
Transformer verbatim in-context retrieval across time and scale (2024)

To predict upcoming text, language models must in some cases retrieve in-context information verbatim. In this report, we investigated how the ability of language models to retrieve arbitrary in-context nouns developed during training (across time) and as language models trained on the same dataset increase in size (across scale). We then asked whether learning of in-context retrieval correlates with learning of more challenging zero-shot benchmarks. Furthermore, inspired by semantic effects in human short-term memory, we evaluated the retrieval with respect to a major semantic component of target nouns, namely whether they denote a concrete or abstract entity, as rated by humans. We show that verbatim in-context retrieval developed in a sudden transition early in the training process, after about 1{\%} of the training tokens. This was observed across model sizes (from 14M and up to 12B parameters), and the transition occurred slightly later for the two smallest models. We further found that the development of verbatim in-context retrieval is positively correlated with the learning of zero-shot benchmarks. Around the transition point, all models showed the advantage of retrieving concrete nouns as opposed to abstract nouns. In all but two smallest models, the advantage dissipated away toward the end of training.