ymoslem/acl-abstracts huggingface dataset:
To Know or Not To Know? Analyzing Self-Consistency of Large Language Models under Ambiguity (2024)

One of the major aspects contributing to the striking performance of large language models (LLMs) is the vast amount of factual knowledge accumulated during pre-training. Yet, many LLMs suffer from self-inconsistency, which raises doubts about their trustworthiness and reliability. This paper focuses on entity type ambiguity, analyzing the proficiency and consistency of state-of-the-art LLMs in applying factual knowledge when prompted with ambiguous entities. To do so, we propose an evaluation protocol that disentangles knowing from applying knowledge, and test state-of-the-art LLMs on 49 ambiguous entities. Our experiments reveal that LLMs struggle with choosing the correct entity reading, achieving an average accuracy of only 85{\%}, and as low as 75{\%} with underspecified prompts. The results also reveal systematic discrepancies in LLM behavior, showing that while the models may possess knowledge, they struggle to apply it consistently, exhibit biases toward preferred readings, and display self-inconsistencies. This highlights the need to address entity ambiguity in the future for more trustworthy LLMs.
Context vs. Human Disagreement in Sarcasm Detection (2024)

Prior work has highlighted the importance of context in the identification of sarcasm by humans and language models. This work examines how much context is required for a better identification of sarcasm by both parties. We collect textual responses to dialogical prompts and sarcasm judgment to the responses placed after long contexts, short contexts, and no contexts. We find that both for humans and language models, the presence of context is generally important in identifying sarcasm in the response. But increasing the amount of context provides no added benefit to humans (long = short {\ensuremath{>}} none). This is the same for language models, but only on easily agreed-upon sentences; for sentences with disagreement among human evaluators, different models show different behavior. We also show how sarcasm detection patterns stay consistent as the amount of context is manipulated despite the low agreement in human evaluation.
Optimizing Multilingual Euphemism Detection using Low-Rank Adaption Within and Across Languages (2024)

This short paper presents an investigation into the effectiveness of various classification methods as a submission in the Multilingual Euphemism Detection Shared Task for the Fourth Workshop on Figurative Language Processing co-located with NAACL 2024. The process used by the participant utilizes pre-trained large language models combined with parameter efficient fine-tuning methods, specifically Low-Rank Adaptation (LoRA), in classifying euphemisms across four different languages - Mandarin Chinese, American English, Spanish, and Yor{\`u}b{\'a}. The study is comprised of three main components that aim to explore heuristic methods to navigate how base models can most efficiently be fine-tuned into classifiers to learn figurative language. Multilingual labeled training data was utilized to fine-tune classifiers for each language, and later combined for one large classifier, while unseen test data was finally used to evaluate the accuracy of the best performing classifiers. In addition, cross-lingual tests were conducted by applying each language`s data on each of the other language`s classifiers. All of the results provide insights into the potential of pre-trained base models combined with LoRA fine-tuning methods in accurately classifying euphemisms across and within different languages.
Comparison of Image Generation Models for Abstract and Concrete Event Descriptions (2024)

With the advent of diffusion-based image generation models such as DALL-E, Stable Diffusion and Midjourney, high quality images can be easily generated using textual inputs. It is unclear, however, to what extent the generated images resemble human mental representations, especially regarding abstract event knowledge. We analyse the capability of four state-of-the-art models in generating images of verb-object event pairs when we systematically manipulate the degrees of abstractness of both the verbs and the object nouns. Human judgements assess the generated images and demonstrate that DALL-E is strongest for event pairs with concrete nouns (e.g., {\textquotedblleft}pour water{\textquotedblright}; {\textquotedblleft}believe person{\textquotedblright}), while Midjourney is preferred for event pairs with abstract nouns (e.g., {\textquotedblleft}raise awareness{\textquotedblright}; {\textquotedblleft}remain mystery{\textquotedblright}), irrespective of the concreteness of the verb. Across models, humans were most unsatisfied with images of events pairs that combined concrete verbs with abstract direct-object nouns (e.g., {\textquotedblleft}speak truth{\textquotedblright}), and an additional ad-hoc annotation contributes this to its potential for figurative language.
Cross-Lingual Metaphor Detection for Low-Resource Languages (2024)

Research on metaphor detection (MD) in a multilingual setup has recently gained momentum. As for many tasks, it is however unclear how the amount of data used to pretrain large language models affects the performance, and whether non-neural models might provide a reasonable alternative, especially for MD in low-resource languages. This paper compares neural and non-neural cross-lingual models for English as the source language and Russian, German and Latin as target languages. In a series of experiments we show that the neural cross-lingual adapter architecture MAD-X performs best across target languages. Zero-shot classification with mBERT achieves decent results above the majority baseline, while few-shot classification with mBERT heavily depends on shot-selection, which is inconvenient in a cross-lingual setup where no validation data for the target language exists. The non-neural model, a random forest classifier with conceptual features, is outperformed by the neural models. Overall, we recommend MAD-X for metaphor detection not only in high-resource but also in low-resource scenarios regarding the amounts of pretraining data for mBERT.
A Hard Nut to Crack: Idiom Detection with Conversational Large Language Models (2024)

In this work, we explore idiomatic language processing with Large Language Models (LLMs). We introduce the Idiomatic language Test Suite IdioTS, a dataset of difficult examples specifically designed by language experts to assess the capabilities of LLMs to process figurative language at sentence level. We propose a comprehensive evaluation methodology based on an idiom detection task, where LLMs are prompted with detecting an idiomatic expression in a given English sentence. We present a thorough automatic and manual evaluation of the results and a comprehensive error analysis.
The Elephant in the Room: Ten Challenges of Computational Detection of Rhetorical Figures (2024)

Computational detection of rhetorical figures focuses mostly on figures such as metaphor, irony, or analogy. However, there exist many more figures that are neither less important nor less prevalent. We wanted to pinpoint the reasons why researchers often avoid other figures and to shed light on the challenges they struggle with when investigating those figures. In this comprehensive survey, we analyzed over 40 papers dealing with the computational detection of rhetorical figures other than metaphor, simile, sarcasm, and irony. We encountered recurrent challenges from which we compiled a ten point list. Furthermore, we suggest solutions for each challenge to encourage researchers to investigate a greater variety of rhetorical figures.
Guidelines for the Annotation of Deliberate Linguistic Metaphor (2024)

This paper presents guidelines for the annotation of deliberate linguistic metaphor. Expressions that contribute to the same metaphorical image are annotated as a chain along with a semantically contrasting expression of the target domain, which helps to make the domain contrast inherent to metaphor more explicit. So far, a corpus of ten TEDx talks with a total of ca. 20k tokens has been annotated according to these guidelines. 1.35{\%} of the tokens are deliberate metaphorical expressions according to our guidelines, which shows that our guidelines successfully identify a significantly higher proportion of deliberate metaphorical expressions than previous studies.
Evaluating the Development of Linguistic Metaphor Annotation in Mexican Spanish Popular Science Tweets (2024)

Following previous work on metaphor annotation and automatic metaphor processing, this study presents the evaluation of an initial phase in the novel area of linguistic metaphor detection in Mexican Spanish popular science tweets. Specifically, we examine the challenges posed by the annotation process stemming from disagreement among annotators. During this phase of our work, we conducted the annotation of a corpus comprising 3733 Mexican Spanish popular science tweets. This corpus was divided into two halves and each half was then assigned to two different pairs of native Mexican Spanish-speaking annotators. Despite rigorous methodology and continuous training, inter-annotator agreement as measured by Cohen`s kappa was found to be low, slightly above chance levels, although the concordance percentage exceeded 60{\%}. By elucidating the inherent complexity of metaphor annotation tasks, our evaluation emphasizes the implications of these findings and offers insights for future research in this field, with the aim of creating a robust dataset for machine learning.
Can GPT4 Detect Euphemisms across Multiple Languages? (2024)

A euphemism is a word or phrase used in place of another word or phrase that might be considered harsh, blunt, unpleasant, or offensive. Euphemisms generally soften the impact of what is being said, making it more palatable or appropriate for the context or audience. Euphemisms can vary significantly between languages, reflecting cultural sensitivities and taboos, and what might be a mild expression in one language could carry a stronger connotation or be completely misunderstood in another. This paper uses prompting techniques to evaluate OpenAI`s GPT4 for detecting euphemisms across multiple languages as part of the 2024 FigLang shared task. We evaluate both zero-shot and few-shot approaches. Our method achieved an average macro F1 of .732, ranking first in the competition. Moreover, we found that GPT4 does not perform uniformly across all languages, with a difference of .233 between the best (English .831) and the worst (Spanish .598) languages.
Ensemble-based Multilingual Euphemism Detection: a Behavior-Guided Approach (2024)

This paper describes the system submitted by our team to the Multilingual Euphemism Detection Shared Task for the Fourth Workshop on Figurative Language Processing (FigLang 2024). We propose a novel model for multilingual euphemism detection, combining contextual and behavior-related features. The system classifies texts that potentially contain euphemistic terms with an ensemble classifier based on outputs from behavior-related fine-tuned models. Our results show that, for this kind of task, our model outperforms baselines and state-of-the-art euphemism detection methods. As for the leader-board, our classification model achieved a macro averaged F1 score of [anonymized], reaching the [anonymized] place.
An Expectation-Realization Model for Metaphor Detection (2024)

We propose a new model for metaphor detection in which an expectation component estimates representations of expected word meanings in a given context, whereas a realization component computes representations of target word meanings in context. We also introduce a systematic evaluation methodology that estimates generalization performance in three settings: within distribution, a new strong out of distribution setting, and a novel out-of-pretraining setting. Across all settings, the expectation-realization model obtains results that are competitive with or better than previous metaphor detection models.
A Textual Modal Supplement Framework for Understanding Multi-Modal Figurative Language (2024)

Figurative language in media such as memes, art, or comics has gained dramatic interest recently. However, the challenge remains in accurately justifying and explaining whether an image caption complements or contradicts the image it accompanies. To tackle this problem, we design a modal-supplement framework MAPPER consisting of a describer and thinker. The describer based on a frozen large vision model is designed to describe an image in detail to capture entailed semantic information. The thinker based on a finetuned large multi-modal model is designed to utilize description, claim and image to make prediction and explanation. Experiment results on a publicly available benchmark dataset from FigLang2024 Task 2 show that our method ranks at top 1 in overall evaluation, the performance exceeds the second place by 28.57{\%}. This indicates that MAPPER is highly effective in understanding, judging and explaining of the figurative language. The source code is available at https://github.com/Libv-Team/figlang2024.
FigCLIP: A Generative Multimodal Model with Bidirectional Cross-attention for Understanding Figurative Language via Visual Entailment (2024)

This is a system paper for the FigLang-2024 Multimodal Figurative Language Shared Task. Figurative language is generally represented through multiple modalities, facilitating the expression of complex and abstract ideas. With the popularity of various text-to-image tools, a large number of images containing metaphors or ironies are created. Traditional recognizing textual entailment has been extended to the task of understanding figurative language via visual entailment. However, existing pre-trained multimodal models in open domains often struggle with this task due to the intertwining of counterfactuals, human culture, and imagination. To bridge this gap, we propose FigCLIP, an end-to-end model based on CLIP and GPT-2, to identify multimodal figurative semantics and generate explanations. It employs a bidirectional fusion module with cross-attention and leverages explanations to promote the alignment of figurative image-text representations. Experimental results on the benchmark demonstrate the effectiveness of our method, achieving 70{\%} F1-score, 67{\%} F1@50-score and 50{\%} F1@60-score. It outperforms GPT-4V, which has robust visual reasoning capabilities.
The Register-specific Distribution of Personification in Hungarian: A Corpus-driven Analysis (2024)

The aim of the paper is twofold: (i) to present an extended version of the PerSE corpus, the language resource for investigating personification in Hungarian; (ii) to explore the semantic and lexicogrammatical patterns of Hungarian personification in a corpus-driven analysis, based on the current version of the research corpus. PerSE corpus is compiled from online available Hungarian texts in different registers including journalistic (car reviews and reports on interstate relations) and academic discourse (original research papers from different fields). The paper provides the reader with the infrastructure and the protocol of the semi-automatic and manual annotation in the corpus. Then it gives an overview of the register-specific distribution of personifications and focuses on some of its lexicogrammatical patterns.
Report on the Multilingual Euphemism Detection Task (2024)

This paper presents the Multilingual Euphemism Detection Shared Task for the Fourth Workshop on Figurative Language Processing (FigLang 2024) held in conjunction with NAACL 2024. Participants were invited to attempt the euphemism detection task on four different languages (American English, global Spanish, Yor{\`u}b{\'a}, and Mandarin Chinese): given input text containing a potentially euphemistic term (PET), determine if its use is euphemistic or not. We present the expanded datasets used for the shared task, summarize each team`s methods and findings, and analyze potential implications for future research.
A Report on the FigLang 2024 Shared Task on Multimodal Figurative Language (2024)

We present the outcomes of the Multimodal Figurative Language Shared Task held at the 4th Workshop on Figurative Language Processing (FigLang 2024) co-located at NAACL 2024. The task utilized the V-FLUTE dataset which is comprised of $<$image, text$>$ pairs that use figurative language and includes detailed textual explanations for the entailment or contradiction relationship of each pair. The challenge for participants was to develop models capable of accurately identifying the visual entailment relationship in these multimodal instances and generating persuasive free-text explanations. The results showed that the participants' models significantly outperformed the initial baselines in both automated and human evaluations. We also provide an overview of the systems submitted and analyze the results of the evaluations. All participating systems outperformed the LLaVA-ZS baseline, provided by us in F1-score.
The Parallel Corpus of Russian and Ruska Romani Languages (2024)

The paper presents a parallel corpus for the Ruska Romani dialect and Russian language. Ruska Romani is the dialect of Romani language attributed to Ruska Roma, the largest subgroup of Romani people in Russia. The corpus contains the translations of Russian literature into Ruska Romani dialect. The corpus creation involved manual alignment of a small part of translations with original works, fine-tuning a language model on the aligned pairs, and using the fine-tuned model to align the remaining data. Ruska Romani sentences were annotated using a morphological analyzer, with rules crafted for proper nouns and borrowings. The corpus, available in JSON and Russian National Corpus XML formats. It includes 88,742 Russian tokens and 84,635 Ruska Romani tokens, 74,291 of which were grammatically annotated. The corpus could be used for linguistic research, including comparative and diachronic studies, bilingual dictionary creation, stylometry research, and NLP/MT tool development for Ruska Romani.
ManWav: The First Manchu ASR Model (2024)

This study addresses the widening gap in Automatic Speech Recognition (ASR) research between high resource and extremely low resource languages, with a particular focus on Manchu, a severely endangered language. Manchu exemplifies the challenges faced by marginalized linguistic communities in accessing state-of-the-art technologies. In a pioneering effort, we introduce the first-ever Manchu ASR model ManWav, leveraging Wav2Vec2-XLSR-53. The results of the first Manchu ASR is promising, especially when trained with our augmented data. Wav2Vec2-XLSR-53 fine-tuned with augmented data demonstrates a 0.02 drop in CER and 0.13 drop in WER compared to the same base model fine-tuned with original data.
User-Centered Design of Digital Tools for Sociolinguistic Studies in Under-Resourced Languages (2024)

Investigating language variation is a core aspect of sociolinguistics, especially through the use of linguistic corpora. Collecting and analyzing spoken language in text-based corpora can be time-consuming and error-prone, especially for under-resourced languages with limited software assistance. This paper explores the language variation research process using a User-Centered Design (UCD) approach from the field of Human-Computer Interaction (HCI), offering guidelines for the development of digital tools for sociolinguists. We interviewed four researchers, observed their workflows and software usage, and analyzed the data using Grounded Theory. This revealed key challenges in manual tasks, software assistance, and data management. Based on these insights, we identified a set of requirements that future tools should meet to be valuable for researchers in this domain. The paper concludes by proposing design concepts with sketches and prototypes based on the identified requirements. These concepts aim to guide the implementation of a fully functional, open-source tool. This work presents an interdisciplinary approach between sociolinguistics and HCI by emphasizing the practical aspects of research that are often overlooked.
Documenting Endangered Languages with LangDoc: A Wordlist-Based System and A Case Study on Moklen (2024)

Language documentation, especially languages lacking standardised writing systems, is a laborious and time-consuming process. This paper introduces LangDoc, a comprehensive system designed to address challenges and improve the efficiency and accuracy of language documentation projects. LangDoc offers several features, including tools for managing, recording, and reviewing the collected data. It operates both online and offline, crucial for fieldwork in remote locations. The paper also presents a comparative analysis demonstrating LangDoc`s efficiency compared to other methods. A case study of the Moklen language documentation project demonstrates how the features address the specific challenges of working with endangered languages and remote communities. Future development areas include integrating with NLP tools for advanced linguistic analysis and emphasising its potential to support the preservation of language diversity.
Leveraging Deep Learning to Shed Light on Tones of an Endangered Language: A Case Study of Moklen (2024)

Moklen, a tonal Austronesian language spoken in Thailand, exhibits two tones with unbalanced distributions. We employed machine learning techniques for time-series classification to investigate its acoustic properties. Our analysis reveals that a synergy between pitch and vowel quality is crucial for tone distinction, as the model trained with these features achieved the highest accuracy.
A Comparative Analysis of Speaker Diarization Models: Creating a Dataset for German Dialectal Speech (2024)

Speaker diarization is a critical task in the field of computer science, aiming to assign timestamps and speaker labels to audio segments. The aim of these tests in this Publication is to find a pretrained speaker diarization pipeline capable of distinguishing dialectal speakers from each other and an explorer. To achieve this, three pipelines, namely Pyannote, CLEAVER and NeMo, are tested and compared, across various segmentation and parameterization strategies. The study considers multiple scenarios, such as the impact of threshold values, overlap handling, and minimum duration parameters, on classification accuracy. Additionally, this study aims to create a dataset for German dialect identification (DID) based on the findings from this research.
Noise Be Gone: Does Speech Enhancement Distort Linguistic Nuances? (2024)

This study evaluates the impact of speech enhancement (SE) techniques on linguistic research, focusing on their ability to maintain essential acoustic characteristics in enhanced audio without introducing significant artifacts. Through a sociophonetic analysis of Peninsular and Peruvian Spanish speakers, using both original and enhanced recordings, we demonstrate that SE effectively preserves critical speech nuances such as voicing and vowel quality. This supports the use of SE in improving the quality of speech samples. This study marks an initial effort to assess SE`s reliability in language studies and proposes a methodology for enhancing low-quality audio corpora of under-resourced languages.