ymoslem/acl-abstracts huggingface dataset:
There`s Something New about the Italian Parliament: The IPSA Corpus (2024)

Parliamentary debates constitute a substantial and somewhat underutilized reservoir of publicly available written content. Despite their potential, the Italian parliamentary documents remain largely unexplored and most importantly inaccessible in their original paper-based form. In this paper we attempt to transform these valuable historical documents into IPSA, a digitally readable structured corpus containing speeches, reports of the Standing Committees, and law proposals spanning 175 years of Italian history, from the issuing of the Statuto Albertino in 1848, up to the present day. At first, the PDF documents, available on the official websites of Senato della Repubblica and Camera dei Deputati, the two chambers that form the Italian Parliament, are digitized using Optical Character Recognition (OCR) techniques. Then, the speeches are tagged with the corresponding speakers. The final dataset is released both in textual and structured format.
The RIP Corpus of Collaborative Hypothesis-Making (2024)

The dearth of literature combining hypothesis-making and collaborative problem solving presents a problem in the investigation into how hypotheses are generated in group environments. A new dataset, the Resolving Investigative hyPotheses (RIP) corpus, is introduced to address this issue. The corpus uses the fictionalised environment of a murder investigation game. An artificial environment restricts the number of possible hypotheses compared to real-world situations, allowing a deeper dive into the data. In three groups of three, participants collaborated to solve the mystery: two groups came to the wrong conclusion in different ways, and one succeeded in solving the game. RIP is a 49k-word dialogical corpus, consisting of three sub-corpora, annotated for argumentation and discourse structure on the basis of Inference Anchoring Theory. The corpus shows the emergent roles individuals took on and the strategies the groups employed, showing what can be gained through a deeper exploration of this domain. The corpus bridges the gap between these two areas {--} hypothesis generation and collaborative problem solving {--} by using an environment rich with potential for hypothesising within a highly collaborative space.
The Role of Creaky Voice in Turn Taking and the Perception of Speaker Stance: Experiments Using Controllable TTS (2024)

Recent advancements in spontaneous text-to-speech (TTS) have enabled the realistic synthesis of creaky voice, a voice quality known for its diverse pragmatic and paralinguistic functions. In this study, we used synthesized creaky voice in perceptual tests, to explore how listeners without formal training perceive two distinct types of creaky voice. We annotated a spontaneous speech corpus using creaky voice detection tools and modified a neural TTS engine with a creaky phonation embedding to control the presence of creaky phonation in the synthesized speech. We performed an objective analysis using a creak detection tool which revealed significant differences in creaky phonation levels between the two creaky voice types and modal voice. Two subjective listening experiments were performed to investigate the effect of creaky voice on perceived certainty, valence, sarcasm, and turn finality. Participants rated non-positional creak as less certain, less positive, and more indicative of turn finality, while positional creak was rated significantly more turn final compared to modal phonation.
The Role of Syntactic Span Preferences in Post-Hoc Explanation Disagreement (2024)

Post-hoc explanation methods are an important tool for increasing model transparency for users. Unfortunately, the currently used methods for attributing token importance often yield diverging patterns. In this work, we study potential sources of disagreement across methods from a linguistic perspective. We find that different methods systematically select different classes of words and that methods that agree most with other methods and with humans display similar linguistic preferences. Token-level differences between methods are smoothed out if we compare them on the syntactic span level. We also find higher agreement across methods by estimating the most important spans dynamically instead of relying on a fixed subset of size k. We systematically investigate the interaction between k and spans and propose an improved configuration for selecting important tokens.
The SAMER Arabic Text Simplification Corpus (2024)

We present the SAMER Corpus, the first manually annotated Arabic parallel corpus for text simplification targeting school-aged learners. Our corpus comprises texts of 159K words selected from 15 publicly available Arabic fiction novels most of which were published between 1865 and 1955. Our corpus includes readability level annotations at both the document and word levels, as well as two simplified parallel versions for each text targeting learners at two different readability levels. We describe the corpus selection process, and outline the guidelines we followed to create the annotations and ensure their quality. Our corpus is publicly available to support and encourage research on Arabic text simplification, Arabic automatic readability assessment, and the development of Arabic pedagogical language technologies.
The Slovak Autistic and Non-Autistic Child Speech Corpus:Task-Oriented Child-Adult Interactions (2024)

This paper presents the Slovak Autistic and Non-Autistic Child Speech Corpus, which consists of audio-recordings and transcripts of collaborative, task-oriented conversations between children (with or without autism spectrum disorder, ASD) and a non-autistic adult experimenter. The task used to elicit this corpus was the Maps task. This corpus was primarily recorded to investigate lexical alignment, but can also be used to study other conversation coordination strategies and behaviours. Scores on various standardised psychometric tests, such as those measuring IQ, executive functioning, and theory of mind, are included for each participant. In total, the corpus contains over 15 hours of speech. This relatively large database contains a non-Germanic language and can be shared with any qualified researcher, making it a valuable resource for replication of existing findings regarding communication and ASD as well as future research into communication between individuals with and without ASD.
The Swedish Parliament Corpus 1867 -- 2022 (2024)

The Swedish parliamentary records are an important source material for social science and humanities researchers. We introduce a new research corpus, the Swedish Parliament Corpus, which is larger and more developed than previously available research corpora for the Swedish parliament. The corpus contains annotated and structured parliamentary records over more than 150 years, through the bicameral parliament (1867{--}1970) and the unicameral parliament (1971{--}). In addition to the records, which contain all speeches in the parliament, we also provide a database of all members of parliament over the same period. Along with the corpus, we describe procedures to ensure data quality. The corpus facilitates detailed analysis of parliamentary speeches in several research fields.
The Syntactic Acceptability Dataset (Preview): A Resource for Machine Learning and Linguistic Analysis of English (2024)

We present a preview of the Syntactic Acceptability Dataset, a resource being designed for both syntax and computational linguistics research. In its current form, the dataset comprises 1,000 English sequences from the syntactic discourse: Half from textbooks and half from the journal Linguistic Inquiry, the latter to ensure a representation of the contemporary discourse. Each entry is labeled with its grammatical status ({\textquotedblleft}well-formedness{\textquotedblright} according to syntactic formalisms) extracted from the literature, as well as its acceptability status ({\textquotedblleft}intuitive goodness{\textquotedblright} as determined by native speakers) obtained through crowdsourcing, with highest experimental standards. Even in its preliminary form, this dataset stands as the largest of its kind that is publicly accessible. We also offer preliminary analyses addressing three debates in linguistics and computational linguistics: We observe that grammaticality and acceptability judgments converge in about 83{\%} of the cases and that {\textquotedblleft}in-betweenness{\textquotedblright} occurs frequently. This corroborates existing research. We also find that while machine learning models struggle with predicting grammaticality, they perform considerably better in predicting acceptability. This is a novel finding. Future work will focus on expanding the dataset.
The Touch\'e23-ValueEval Dataset for Identifying Human Values behind Arguments (2024)

While human values play a crucial role in making arguments persuasive, we currently lack the necessary extensive datasets to develop methods for analyzing the values underlying these arguments on a large scale. To address this gap, we present the Touch{\'e}23-ValueEval dataset, an expansion of the Webis-ArgValues-22 dataset. We collected and annotated an additional 4780 new arguments, doubling the dataset`s size to 9324 arguments. These arguments were sourced from six diverse sources, covering religious texts, community discussions, free-text arguments, newspaper editorials, and political debates. Each argument is annotated by three crowdworkers for 54 human values, following the methodology established in the original dataset. The Touch{\'e}23-ValueEval dataset was utilized in the SemEval 2023 Task 4. ValueEval: Identification of Human Values behind Arguments, where an ensemble of transformer models demonstrated state-of-the-art performance. Furthermore, our experiments show that a fine-tuned large language model, Llama-2-7B, achieves comparable results.
TIGER: A Unified Generative Model Framework for Multimodal Dialogue Response Generation (2024)

Responding with multimodal content has been recognized as one of the essential functionalities of intelligent conversational agents. However, existing research on multimodal dialogues primarily focuses on two topics: (1) textual response generation that ground the conversation on a given image; and (2) visual response selection based on the dialogue context. In light of the aforementioned gap, we propose mulTImodal GEnerator for dialogue Response (TIGER), a unified generative model framework for multimodal dialogue response generation. Through extensive experiments, TIGER has demonstrated new state-of-the-art results, providing users with an enhanced conversational experience. A multimodal dialogue system based on TIGER is available at https://github.com/friedrichor/TIGER. A video demonstrating the system is available at https://www.youtube.com/watch?v=Kd0CMwDs8Rk.
TIGQA: An Expert-Annotated Question-Answering Dataset in Tigrinya (2024)

The absence of explicitly tailored, accessible annotated datasets for educational purposes presents a notable obstacle for NLP tasks in languages with limited resources. This study initially explores the feasibility of using machine translation (MT) to convert an existing dataset into a Tigrinya dataset in SQuAD format. As a result, we present TIGQA, an expert-annotated dataset containing 2,685 question-answer pairs covering 122 diverse topics such as climate, water, and traffic. These pairs are from 537 context paragraphs in publicly accessible Tigrinya and Biology books. Through comprehensive analyses, we demonstrate that the TIGQA dataset requires skills beyond simple word matching, requiring both single-sentence and multiple-sentence inference abilities. We conduct experiments using state-of-the-art MRC methods, marking the first exploration of such models on TIGQA. Additionally, we estimate human performance on the dataset and juxtapose it with the results obtained from pre-trained models. The notable disparities between human performance and the best model performance underscore the potential for fu- ture enhancements to TIGQA through continued research. Our dataset is freely accessible via the provided link to encourage the research community to address the challenges in the Tigrinya MRC. Keywords: Tigrinya QA dataset, Low resource QA dataset, domain specific QA
Time-aware COMET: A Commonsense Knowledge Model with Temporal Knowledge (2024)

To better handle commonsense knowledge, which is difficult to acquire in ordinary training of language models, commonsense knowledge graphs and commonsense knowledge models have been constructed. The former manually and symbolically represents commonsense, and the latter stores these graphs' knowledge in the models' parameters. However, the existing commonsense knowledge models that deal with events do not consider granularity or time axes. In this paper, we propose a time-aware commonsense knowledge model, TaCOMET. The construction of TaCOMET consists of two steps. First, we create TimeATOMIC using ChatGPT, which is a commonsense knowledge graph with time. Second, TaCOMET is built by continually finetuning an existing commonsense knowledge model on TimeATOMIC. TimeATOMIC and continual finetuning let the model make more time-aware generations with rich commonsense than the existing commonsense models. We also verify the applicability of TaCOMET on a robotic decision-making task. TaCOMET outperformed the existing commonsense knowledge model when proper times are input. Our dataset and models will be made publicly available.
Title-based Extractive Summarization via MRC Framework (2024)

Existing studies on extractive summarization have primarily focused on scoring and selecting summary sentences independently. However, these models are limited to sentence-level extraction and tend to select highly generalized sentences while overlooking the overall content of a document. To effectively consider the semantics of a document, in this study, we introduce a novel machine reading comprehension (MRC) framework for extractive summarization (MRCSum) by setting a query as the title. Our framework enables MRCSum to consider the semantic coherence and relevance of summary sentences in relation to the overall content. In particular, when a title is not available, we generate a title-like query, which is expected to achieve the same effect as a title. Our title-like query consists of the topic and keywords to serve as information on the main topic or theme of the document. We conduct experiments in both Korean and English languages, evaluating the performance of MRCSum on datasets comprising both long and short summaries. Our results demonstrate the effectiveness of MRCSum in extractive summarization, showcasing its ability to generate concise and informative summaries with or without explicit titles. Furthermore, our MRCSum outperforms existing models by capturing the essence of the document content and producing more coherent summaries.
TMFN: A Target-oriented Multi-grained Fusion Network for End-to-end Aspect-based Multimodal Sentiment Analysis (2024)

End-to-end multimodal aspect-based sentiment analysis (MABSA) combines multimodal aspect terms extraction (MATE) with multimodal aspect sentiment classification (MASC), aiming to simultaneously extract aspect words and classify the sentiment polarity of each aspect. However, existing MABSA methods have overlooked two issues: (i) They only focus on fusing image regional information and textual words for two subtasks of MABSA. Whereas, MATE subtask relies more on global image information to assist in obtaining the quantity and attributes of aspects. Ignoring the integration with global information may affect the performance of MABSA methods. (ii) They fail to take advantage of target information. Nevertheless, the fine-grained details of targets are important for classifying sentiments of aspects. To solve these problems, we propose a Target-oriented Multi-grained Fusion Network(TMFN). It fuses text information with global coarse-grained image information for MATE subtask and with fine-grained image information for MASC subtask. In addition, a target-oriented feature alignment (TOFA) module is designed to enhance target-related information in image features with target details. In such a way, image features will contain more target emotional-related information which is beneficial to sentiment classification. Extensive experiments show that our method outperforms state-of-the-art methods on two benchmark datasets.
To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in Japanese (2024)

Speakers sometimes omit certain arguments of a predicate in a sentence; such omission is especially frequent in pro-drop languages. This study addresses a question about ellipsis{---}what can explain the native speakers' ellipsis decisions?{---}motivated by the interest in human discourse processing and writing assistance for this choice. To this end, we first collect large-scale human annotations of whether and why a particular argument should be omitted across over 2,000 data points in the balanced corpus of Japanese, a prototypical pro-drop language. The data indicate that native speakers overall share common criteria for such judgments and further clarify their quantitative characteristics, e.g., the distribution of related linguistic factors in the balanced corpus. Furthermore, the performance of the language model{--}based argument ellipsis judgment model is examined, and the gap between the systems' prediction and human judgments in specific linguistic aspects is revealed. We hope our fundamental resource encourages further studies on natural human ellipsis judgment.
To Err Is Human, How about Medical Large Language Models? Comparing Pre-trained Language Models for Medical Assessment Errors and Reliability (2024)

Unpredictability, especially unpredictability with unknown error characteristics, is a highly undesirable trait, particularly in medical patient care applications. Although large pre-trained language models (LLM) have been applied to a variety of unseen tasks with highly competitive and successful results, their sensitivity to language inputs and resulting performance variability is not well-studied. In this work, we test state-of-the-art pre-trained language models from a variety of families to characterize their error generation and reliability in medical assessment ability. Particularly, we experiment with general medical assessment multiple choice tests, as well as their open-ended and true-false alternatives. We also profile model consistency, error agreements with each other and to humans; and finally, quantify their ability to recover and explain errors. The findings in this work can be used to give further information about medical models so that modelers can make better-informed decisions rather than relying on standalone performance metrics alone.
Token-length Bias in Minimal-pair Paradigm Datasets (2024)

Minimal-pair paradigm datasets have been used as benchmarks to evaluate the linguistic knowledge of models and provide an unsupervised method of acceptability judgment. The model performances are evaluated based on the percentage of minimal pairs in the MPP dataset where the model assigns a higher sentence log-likelihood to an acceptable sentence than to an unacceptable sentence. Each minimal pair in MPP datasets is controlled to align the number of words per sentence because the sentence length affects the sentence log-likelihood. However, aligning the number of words may be insufficient because recent language models tokenize sentences with subwords. Tokenization may cause a token length difference in minimal pairs, introducing token-length bias that skews the evaluation results. This study demonstrates that MPP datasets suffer from token-length bias and fail to evaluate the linguistic knowledge of a language model correctly. The results proved that sentences with a shorter token length would likely be assigned a higher log-likelihood regardless of their acceptability, which becomes problematic when comparing models with different tokenizers. To address this issue, we propose a debiased minimal pair generation method, allowing MPP datasets to measure language ability correctly and provide comparable results for all models.
To Learn or Not to Learn: Replaced Token Detection for Learning the Meaning of Negation (2024)

State-of-the-art language models perform well on a variety of language tasks, but they continue to struggle with understanding negation cues in tasks like natural language inference (NLI). Inspired by Hossain et al. (2020), who show under-representation of negation in language model pretraining datasets, we experiment with additional pretraining with negation data for which we introduce two new datasets. We also introduce a new learning strategy for negation building on ELECTRA`s (Clark et al., 2020) replaced token detection objective. We find that continuing to pretrain ELECTRA-Small`s discriminator leads to substantial gains on a variant of RTE (Recognizing Textual Entailment) with additional negation. On SNLI (Stanford NLI) (Bowman et al., 2015), there are no gains due to the extreme under-representation of negation in the data. Finally, on MNLI (Multi-NLI) (Williams et al., 2018), we find that performance on negation cues is primarily stymied by neutral-labeled examples.
ToNER: Type-oriented Named Entity Recognition with Generative Language Model (2024)

In recent years, the fine-tuned generative models have been proven more powerful than the previous tagging-based or span-based models on named entity recognition (NER) task. It has also been found that the information related to entities, such as entity types, can prompt a model to achieve NER better. However, it is not easy to determine the entity types indeed existing in the given sentence in advance, and inputting too many potential entity types would distract the model inevitably. To exploit entity types' merit on promoting NER task, in this paper we propose a novel NER framework, namely ToNER based on a generative model. In ToNER, a type matching model is proposed at first to identify the entity types most likely to appear in the sentence. Then, we append a multiple binary classification task to fine-tune the generative model`s encoder, so as to generate the refined representation of the input sentence. Moreover, we add an auxiliary task for the model to discover the entity types which further fine-tunes the model to output more accurate results. Our extensive experiments on some NER benchmarks verify the effectiveness of our proposed strategies in ToNER that are oriented towards entity types' exploitation.
ToolRerank: Adaptive and Hierarchy-Aware Reranking for Tool Retrieval (2024)

Tool learning aims to extend the capabilities of large language models (LLMs) with external tools. A major challenge in tool learning is how to support a large number of tools, including unseen tools. To address this challenge, previous studies have proposed retrieving suitable tools for the LLM based on the user query. However, previously proposed methods do not consider the differences between seen and unseen tools, nor do they take the hierarchy of the tool library into account, which may lead to suboptimal performance for tool retrieval. Therefore, to address the aforementioned issues, we propose ToolRerank, an adaptive and hierarchy-aware reranking method for tool retrieval to further refine the retrieval results. Specifically, our proposed ToolRerank includes Adaptive Truncation, which truncates the retrieval results related to seen and unseen tools at different positions, and Hierarchy-Aware Reranking, which makes retrieval results more concentrated for single-tool queries and more diverse for multi-tool queries. Experimental results show that ToolRerank can improve the quality of the retrieval results, leading to better execution results generated by the LLM.