ymoslem/acl-abstracts huggingface dataset:
InteRead: An Eye Tracking Dataset of Interrupted Reading (2024)

Eye movements during reading offer a window into cognitive processes and language comprehension, but the scarcity of reading data with interruptions {--} which learners frequently encounter in their everyday learning environments {--} hampers advances in the development of intelligent learning technologies. We introduce InteRead {--} a novel 50-participant dataset of gaze data recorded during self-paced reading of real-world text. InteRead further offers fine-grained annotations of interruptions interspersed throughout the text as well as resumption lags incurred by these interruptions. Interruptions were triggered automatically once readers reached predefined target words. We validate our dataset by reporting interdisciplinary analyses on different measures of gaze behavior. In line with prior research, our analyses show that the interruptions as well as word length and word frequency effects significantly impact eye movements during reading. We also explore individual differences within our dataset, shedding light on the potential for tailored educational solutions. InteRead is accessible from our datasets web-page: https://www.ife.uni-stuttgart.de/en/llis/research/datasets/.
Interpretable Assessment of Speech Intelligibility Using Deep Learning: A Case Study on Speech Disorders Due to Head and Neck Cancers (2024)

This paper sheds light on a relatively unexplored area which is deep learning interpretability for speech disorder assessment and characterization. Building upon a state-of-the-art methodology for the explainability and interpretability of hidden representation inside a deep-learning speech model, we provide a deeper understanding and interpretation of the final intelligibility assessment of patients experiencing speech disorders due to Head and Neck Cancers (HNC). Promising results have been obtained regarding the prediction of speech intelligibility and severity of HNC patients while giving relevant interpretations of the final assessment both at the phonemes and phonetic feature levels. The potential of this approach becomes evident as clinicians can acquire more valuable insights for speech therapy. Indeed, this can help identify the specific linguistic units that affect intelligibility from an acoustic point of view and enable the development of tailored rehabilitation protocols to improve the patient`s ability to communicate effectively, and thus, the patient`s quality of life.
Interpretable Short Video Rumor Detection Based on Modality Tampering (2024)

With the rapid development of social media and short video applications in recent years, browsing short videos has become the norm. Due to its large user base and unique appeal, spreading rumors via short videos has become a severe social problem. Many methods simply fuse multimodal features for rumor detection, which lack interpretability. For short video rumors, rumor makers create rumors by modifying and/or splicing different modal information, so we should consider how to detect rumors from the perspective of modality tampering. Inspired by cross-modal contrastive learning, we propose a novel short video rumor detection framework by designing two pretraining tasks: modality tampering detection and inter-modal matching, imbuing the model with the ability to detect modality tampering and employing it for downstream rumor detection tasks. In addition, we design an interpretability mechanism to make the rumor detection results more reasonable by backtracking the model`s decision-making process. The experimental results show that the method on the short video rumor dataset has an improvement of about 4.6{\%}-12{\%} in macro-F1 compared with other models and can explain whether the short video is a rumor or not through the perspective of modality tampering.
Interpreting Themes from Educational Stories (2024)

Reading comprehension continues to be a crucial research focus in the NLP community. Recent advances in Machine Reading Comprehension (MRC) have mostly centered on literal comprehension, referring to the surface-level understanding of content. In this work, we focus on the next level - interpretive comprehension, with a particular emphasis on inferring the themes of a narrative text. We introduce the first dataset specifically designed for interpretive comprehension of educational narratives, providing corresponding well-edited theme texts. The dataset spans a variety of genres and cultural origins and includes human-annotated theme keywords with varying levels of granularity. We further formulate NLP tasks under different abstractions of interpretive comprehension toward the main idea of a story. After conducting extensive experiments with state-of-the-art methods, we found the task to be both challenging and significant for NLP research. The dataset and source code have been made publicly available to the research community at https://github.com/RiTUAL-UH/EduStory.
Intrinsic Subgraph Generation for Interpretable Graph Based Visual Question Answering (2024)

The large success of deep learning based methods in Visual Question Answering (VQA) has concurrently increased the demand for explainable methods. Most methods in Explainable Artificial Intelligence (XAI) focus on generating post-hoc explanations rather than taking an intrinsic approach, the latter characterizing an interpretable model. In this work, we introduce an interpretable approach for graph-based VQA and demonstrate competitive performance on the GQA dataset. This approach bridges the gap between interpretability and performance. Our model is designed to intrinsically produce a subgraph during the question-answering process as its explanation, providing insight into the decision making. To evaluate the quality of these generated subgraphs, we compare them against established post-hoc explainability methods for graph neural networks, and perform a human evaluation. Moreover, we present quantitative metrics that correlate with the evaluations of human assessors, acting as automatic metrics for the generated explanatory subgraphs. Our code will be made publicly available at link removed due to anonymity period.
Introducing a Parsed Corpus of Historical High German (2024)

We outline the ongoing development of the Indiana Parsed Corpus of (Historical) High German. Once completed, this corpus will fill the gap in Penn-style treebanks for Germanic languages by spanning High German from 1050 to 1950. This paper describes the process of building the corpus: selection of texts, decisions on part-of-speech tags and other labels, the process of annotation, and illustrative annotation issues unique to historical High German. The construction of the corpus has led to a refinement of the Penn labels, tailored to the particulars of this language.
Introducing CQuAE : A New French Contextualised Question-Answering Corpus for the Education Domain (2024)

We present a new question answering corpus in French designed to educational domain. To be useful in such domain, we have to propose more complex questions and to be able to justify the answers on validated material. We analyze some properties of this corpus. The last part of this paper will be devoted to present the first experiments we have carried out to demonstrate the value of this dataset for learning a Retrieval Augmented Genration framework. Different experiments are proposed, with an automatic evaluation. A human evaluation is proposed to confirm or infirm this automatic evaluation.
Investigating the Robustness of Modelling Decisions for Few-Shot Cross-Topic Stance Detection: A Preregistered Study (2024)

For a viewpoint-diverse news recommender, identifying whether two news articles express the same viewpoint is essential. One way to determine {\textquotedblleft}same or different{\textquotedblright} viewpoint is stance detection. In this paper, we investigate the robustness of operationalization choices for few-shot stance detection, with special attention to modelling stance across different topics. Our experiments test pre-registered hypotheses on stance detection. Specifically, we compare two stance task definitions (Pro/Con versus Same Side Stance), two LLM architectures (bi-encoding versus cross-encoding), and adding Natural Language Inference knowledge, with pre-trained RoBERTa models trained with shots of 100 examples from 7 different stance detection datasets. Some of our hypotheses and claims from earlier work can be confirmed, while others give more inconsistent results. The effect of the Same Side Stance definition on performance differs per dataset and is influenced by other modelling choices. We found no relationship between the number of training topics in the training shots and performance. In general, cross-encoding out-performs bi-encoding, and adding NLI training to our models gives considerable improvement, but these results are not consistent across all datasets. Our results indicate that it is essential to include multiple datasets and systematic modelling experiments when aiming to find robust modelling choices for the concept {\textquoteleft}stance'.
IR2: Information Regularization for Information Retrieval (2024)

Effective information retrieval (IR) in settings with limited training data, particularly for complex queries, remains a challenging task. This paper introduces IR2, Information Regularization for Information Retrieval, a technique for reducing overfitting during synthetic data generation. This approach, representing a novel application of regularization techniques in synthetic data creation for IR, is tested on three recent IR tasks characterized by complex queries: DORIS-MAE, ArguAna, and WhatsThatBook. Experimental results indicate that our regularization techniques not only outperform previous synthetic query generation methods on the tasks considered but also reduce cost by up to 50{\%}. Furthermore, this paper categorizes and explores three regularization methods at different stages of the query synthesis pipeline{---}input, prompt, and output{---}each offering varying degrees of performance improvement compared to models where no regularization is applied. This provides a systematic approach for optimizing synthetic data generation in data-limited, complex-query IR scenarios. All code, prompts and synthetic data are available at https://github.com/Info-Regularization/Information-Regularization.
I Remember You!: SUI Corpus for Remembering and Utilizing Users' Information in Chat-oriented Dialogue Systems (2024)

To construct a chat-oriented dialogue system that will be used for a long time by users, it is important to build a good relationship between the user and the system. To achieve a good relationship, several methods for remembering and utilizing information on users (preferences, experiences, jobs, etc.) in system utterances have been investigated. One way to do this is to utilize user information to fill in utterance templates for use in response generation, but the utterances do not always fit the context. Another way is to use neural-based generation, but in current methods, user information can be incorporated only when the current dialogue topic is similar to that of the user information. This paper tackled these problems by constructing a novel corpus to incorporate arbitrary user information into system utterances regardless of the current dialogue topic while retaining appropriateness for the context. We then fine-tuned a model for generating system utterances using the constructed corpus. The result of a subjective evaluation demonstrated the effectiveness of our model. Furthermore, we incorporated our fine-tuned model into a dialogue system and confirmed the effectiveness of the system through interactive dialogues with users.
\`Ir\`oy\`inSpeech: A Multi-purpose Yor\`ub\'a Speech Corpus (2024)

We introduce {\`I}r{\`o}y{\`i}nSpeech corpus{---}a new dataset influenced by a desire to increase the amount of high quality, freely available, contemporary Yor{\`u}b{\'a} speech data that can be used for both Text-to-Speech (TTS) and Automatic Speech Recognition (ASR) tasks. We curated about 23,000 text sentences from the news and creative writing domains with an open license i.e., CC-BY-4.0 and asked multiple speakers to record each sentence. To encourage more participatory approach to data creation, we provide 5 000 utterances from the curated sentences to the Mozilla Common Voice platform to crowd-source the recording and validation of Yor{\`u}b{\'a} speech data. In total, we created about 42 hours of speech data recorded by 80 volunteers in-house, and 6 hours validated recordings on Mozilla Common Voice platform. Our evaluation on TTS shows that we can create a good quality general domain single-speaker TTS model for Yor{\`u}b{\'a} with as little 5 hours of speech by leveraging an end-to-end VITS architecture. Similarly, for ASR, we obtained a WER of 21.5.
Is Crowdsourcing Breaking Your Bank? Cost-Effective Fine-Tuning of Pre-trained Language Models with Proximal Policy Optimization (2024)

Wide usage of ChatGPT has highlighted the potential of reinforcement learning from human feedback. However, its training pipeline relies on manual ranking, a resource-intensive process. To reduce labor costs, we propose a self-supervised text ranking approach for applying Proximal-Policy-Optimization to fine-tune language models while eliminating the need for human annotators. Our method begins with probabilistic sampling to encourage a language model to generate diverse responses for each input. We then employ TextRank and ISODATA algorithms to rank and cluster these responses based on their semantics. Subsequently, we construct a reward model to learn the rank and optimize our generative policy. Our experimental results, conducted using two language models on three tasks, demonstrate that the models trained by our method considerably outperform baselines regarding BLEU, GLEU, and METEOR scores. Furthermore, our manual evaluation shows that our ranking results exhibit a remarkably high consistency with that of humans. This research significantly reduces training costs of proximal policy-guided models and demonstrates the potential for self-correction of language models.
Is Gender Reference Gender-specific? Studies in a Polar Domain (2024)

We investigate how gender authorship influences polar, i.e. positive and negative gender reference. Given German-language newspaper texts where the full name of the authors are known and their gender can be inferred from the first names. And given that nouns in the text have gender reference, i.e. are labeled by a gender classifier as female or male denoting nouns. If these nouns carry a polar load, they count towards the gender-specific statistics we are interested in. A polar load is given either via phrase-level sentiment composition, or by a verb-based analysis of the polar role a noun (phrase) plays: is it framed by the verb as a positive or negative actor, or as receiving a positive or negative effect? Also, reported gender-gender relations (in favor, against) might be gender-specific. Statistical hypothesis testing is carried out in order to find out whether significant gender-wise correlations exist. We found that, in fact, gender reference is gender-specific: each gender significantly more often focuses on their own gender than the other one and e.g. positive actorship supremacy is claimed (intra-) gender-wise.
Is It Possible to Modify Text to a Target Readability Level? An Initial Investigation Using Zero-Shot Large Language Models (2024)

Text simplification is a common task where the text is adapted to make it easier to understand. Similarly, text elaboration can make a passage more sophisticated, offering a method to control the complexity of reading comprehension tests. However, text simplification and elaboration tasks are limited to only relatively alter the readability of texts. It is useful to directly modify the readability of any text to an absolute target readability level to cater to a diverse audience. Ideally, the readability of readability-controlled generated text should be independent of the source text. Therefore, we propose a novel readability-controlled text modification task. The task requires the generation of 8 versions at various target readability levels for each input text. We introduce novel readability-controlled text modification metrics. The baselines for this task use ChatGPT and Llama-2, with an extension approach introducing a two-step process (generating paraphrases by passing through the language model twice). The zero-shot approaches are able to push the readability of the paraphrases in the desired direction but the final readability remains correlated with the original text`s readability. We also find greater drops in semantic and lexical similarity between the source and target texts with greater shifts in the readability.
Is LLM a Reliable Reviewer? A Comprehensive Evaluation of LLM on Automatic Paper Reviewing Tasks (2024)

The use of large language models (LLM), especially ChatGPT, to help with research has come into practice. Researchers use it for timely advice and hope to obtain in-depth feedback. However, can LLM be a qualified and reliable reviewer? Although there already exist several review-related datasets, few works have carefully and thoroughly inspected model`s capability as a reviewer, especially the correctness of generated reviews. In this paper, we first evaluate GPT-3.5 and GPT-4 (the current top-performing LLM) on 2 types of tasks under different settings: the score prediction task and the review generation task. In addition, we propose a dataset containing 197 review-revision multiple-choice questions (RR-MCQ) with detailed labels from the review-rebuttal forum in ICLR-2023. By asking questions from technical details to the overall presentation and quality, our RR-MCQ data provides a more complete model ability assessment. The results show that LLM is generally helpful, but great caution is needed as it always makes mistakes. Although it can give passable decisions ({\ensuremath{>}} 60{\%} accuracy) on single options, completely correct answers are still rare (about 20{\%}); models are still weak on long paper processing, zero-shot scoring, and giving critical feedback like human reviewers.
Is Modularity Transferable? A Case Study through the Lens of Knowledge Distillation (2024)

The rise of Modular Deep Learning showcases its potential in various Natural Language Processing applications. Parameter-efficient fine-tuning (PEFT) modularity has been shown to work for various use cases, from domain adaptation to multilingual setups. However, all this work covers the case where the modular components are trained and deployed within one single Pre-trained Language Model (PLM). This model-specific setup is a substantial limitation on the very modularity that modular architectures are trying to achieve. We ask whether current modular approaches are transferable between models and whether we can transfer the modules from more robust and larger PLMs to smaller ones. In this work, we aim to fill this gap via a lens of Knowledge Distillation, commonly used for model compression, and present an extremely straightforward approach to transferring pre-trained, task-specific PEFT modules between same-family PLMs. Moreover, we propose a method that allows the transfer of modules between incompatible PLMs without any change in the inference complexity. The experiments on Named Entity Recognition, Natural Language Inference, and Paraphrase Identification tasks over multiple languages and PEFT methods showcase the initial potential of transferable modularity.
ISO 24617-12: A New Standard for Semantic Annotation (2024)

This paper presents ISO 24617-12, an annotation scheme for quantification phenomena in natural language., as part of the ISO Semantic Annotation Framework (ISO 24617). This scheme combines ideas from the theory of generalised quantifiers, from neo-Davidsonian event semantics, and from Discourse Representation Theory. The scheme consists of (1) an abstract syntax which defines {\textquoteleft}annotation structures' as triples and other set-theoretic constructs of quantification-related concepts; (2) a reference representation of annotation structures ({\textquoteleft}concrete syntax'); and (3) a compositional semantics of annotation structures. Together, these components define the markup language QuantML. This paper focuses on the identification and structuring of the semantic information useful for the characterisation of quantification in natural language and the interoperable representation of these information structures in QuantML.
IsraParlTweet: The Israeli Parliamentary and Twitter Resource (2024)

We introduce IsraParlTweet, a new linked corpus of Hebrew-language parliamentary discussions from the Knesset (Israeli Parliament) between the years 1992-2023 and Twitter posts made by Members of the Knesset between the years 2008-2023, containing a total of 294.5 million Hebrew tokens. In addition to raw text, the corpus contains comprehensive metadata on speakers and Knesset sessions as well as several linguistic annotations. As a result, IsraParlTweet can be used to conduct a wide variety of quantitative and qualitative analyses and provide valuable insights into political discourse in Israel.
Is Spoken Hungarian Low-resource?: A Quantitative Survey of Hungarian Speech Data Sets (2024)

Even though various speech data sets are available in Hungarian, there is a lack of a general overview about their types and sizes. To fill in this gap, we provide a survey of available data sets in spoken Hungarian in five categories (e.g., monolingual, Hungarian part of multilingual, pathological, child-related and dialectal collections). In total, the estimated size of available data is about 2800 hours (across 7500 speakers) and it represents a rich spoken language diversity. However, the distribution of the data and its alignment to real-life (e.g. speech recognition) tasks is far from optimal indicating the need for additional larger-scale natural language speech data sets. Our survey presents an overview of available data sets for Hungarian explaining their strengths and weaknesses which is useful for researchers working on Hungarian across disciplines. In addition, our survey serves as a starting point towards a unified foundational speech model specific to Hungarian.
Is Summary Useful or Not? An Extrinsic Human Evaluation of Text Summaries on Downstream Tasks (2024)

Research on automated text summarization typically uses human and automatic evaluation methods. While most recent studies focus on intrinsic evaluation, which assesses the general quality of summaries, e.g. coherence and informativeness, we concentrate on task-based extrinsic evaluation to determine the usefulness of summaries. We incorporate three downstream tasks, namely question answering, text classification, and text similarity assessment, and measure the usefulness of summaries for these tasks by several metrics. Our findings reveal that summaries are generally useful in tasks that require a comprehensive grasp of the text but are less useful in tasks requiring a more specific understanding of the text. We also analyze the usefulness and inherent properties of summaries from different models, and find that fine-tuned models consistently produce more useful summaries across all three tasks. In contrast, zero-shot models tend to lean towards text classification and similarity assessment, providing more general and less detailed summaries. Additionally, we assess the correlation between 14 intrinsic automatic metrics and human judgments. Intrinsic metrics perform well in evaluating summaries for question answering but are less effective in the other two tasks. This highlights the limitations of relying solely on intrinsic metrics for assessing summary performance and usefulness.