ymoslem/acl-abstracts huggingface dataset:
Social Convos: Capturing Agendas and Emotions on Social Media (2024)

Social media platforms are popular tools for disseminating targeted information during major public events like elections or pandemics. Systematic analysis of the message traffic can provide valuable insights into prevailing opinions and social dynamics among different segments of the population. We are specifically interested in influence spread, and in particular whether more deliberate influence operations can be detected. However, filtering out the essential messages with telltale influence indicators from the extensive and often chaotic social media traffic is a major challenge.In this paper we present a novel approach to extract influence indicators from messages circulating among groups of users discussing particular topics. We build upon the the concept of a convo to identify influential authors who are actively promoting some particular agenda around that topic within the group. We focus on two influence indicators: the (control of) agenda and the use of emotional language.
Social Orientation: A New Feature for Dialogue Analysis (2024)

There are many settings where it is useful to predict and explain the success or failure of a dialogue. Circumplex theory from psychology models the social orientations (e.g., Warm-Agreeable, Arrogant-Calculating) of conversation participants and can be used to predict and explain the outcome of social interactions. Our work is novel in its systematic application of social orientation tags to modeling conversation outcomes. In this paper, we introduce a new data set of dialogue utterances machine-labeled with social orientation tags. We show that social orientation tags improve task performance, especially in low-resource settings, on both English and Chinese language benchmarks. We also demonstrate how social orientation tags help explain the outcomes of social interactions when used in neural models. Based on these results showing the utility of social orientation tags for dialogue outcome prediction tasks, we release our data sets, code, and models that are fine-tuned to predict social orientation tags on dialogue utterances.
SoftMCL: Soft Momentum Contrastive Learning for Fine-grained Sentiment-aware Pre-training (2024)

The pre-training for language models captures general language understanding but fails to distinguish the affective impact of a particular context to a specific word. Recent works have sought to introduce contrastive learning (CL) for sentiment-aware pre-training in acquiring affective information. Nevertheless, these methods present two significant limitations. First, the compatibility of the GPU memory often limits the number of negative samples, hindering the opportunities to learn good representations. In addition, using only a few sentiment polarities as hard labels, e.g., positive, neutral, and negative, to supervise CL will force all representations to converge to a few points, leading to the issue of latent space collapse. This study proposes a soft momentum contrastive learning (SoftMCL) for fine-grained sentiment-aware pre-training. Instead of hard labels, we introduce valence ratings as soft-label supervision for CL to fine-grained measure the sentiment similarities between samples. The proposed SoftMCL conducts CL on both the word- and sentence-level to enhance the model`s ability to learn affective information. A momentum queue was introduced to expand the contrastive samples, allowing storing and involving more negatives to overcome the limitations of hardware platforms. Extensive experiments were conducted on four different sentiment-related tasks, which demonstrates the effectiveness of the proposed SoftMCL method. The code and data of the proposed SoftMCL is available at: https://www.github.com/wangjin0818/SoftMCL/.
Soft-Prompting with Graph-of-Thought for Multi-modal Representation Learning (2024)

The chain-of-thought technique has been received well in multi-modal tasks. It is a step-by-step linear reasoning process that adjusts the length of the chain to improve the performance of generated prompts. However, human thought processes are predominantly non-linear, as they encompass multiple aspects simultaneously and employ dynamic adjustment and updating mechanisms. Therefore, we propose a novel Aggregation-Graph-of-Thought (AGoT) mechanism for soft-prompt tuning in multi-modal representation learning. The proposed AGoT models the human thought process not only as a chain but also models each step as a reasoning aggregation graph to cope with the overlooked multiple aspects of thinking in single-step reasoning. This turns the entire reasoning process into prompt aggregation and prompt flow operations. Experiments show that our multi-modal model enhanced with AGoT soft-prompting achieves good results in several tasks such as text-image retrieval, visual question answering, and image recognition. In addition, we demonstrate that it has good domain generalization performance due to better reasoning.
Soft Well-Formed Semantic Parsing with Score-Based Selection (2024)

Semantic parsing is the task of translating natural language into a structured, formal semantic representation that can be interpreted by machines. These semantic representations are organized with complex structures. While various models have been developed for semantic parsing, there has been limited focus on generating semantic representations with well-formed structures. In this study, we introduce a score-based method to select well-formed outputs from candidates generated by beam search algorithms. Our experiments focus on parsing texts into discourse representation structures, which are innovative semantic representations designed to capture the meaning of texts with arbitrary lengths across languages. Our experimental results demonstrate that models utilizing the proposed method can reduce the number of ill-formed outputs and improve F1 scores in English. Furthermore, our final model achieves significant improvements in German, Italian and Dutch zero-shot DRS parsing by effectively preventing ill-formed outputs.
So Hateful! Building a Multi-Label Hate Speech Annotated Arabic Dataset (2024)

Social media enables widespread propagation of hate speech targeting groups based on ethnicity, religion, or other characteristics. With manual content moderation being infeasible given the volume, automatic hate speech detection is essential. This paper analyzes 70,000 Arabic tweets, from which 15,965 tweets were selected and annotated, to identify hate speech patterns and train classification models. Annotators labeled the Arabic tweets for offensive content, hate speech, emotion intensity and type, effect on readers, humor, factuality, and spam. Key findings reveal 15{\%} of tweets contain offensive language while 6{\%} have hate speech, mostly targeted towards groups with common ideological or political affiliations. Annotations capture diverse emotions, and sarcasm is more prevalent than humor. Additionally, 10{\%} of tweets provide verifiable factual claims, and 7{\%} are deemed important. For hate speech detection, deep learning models like AraBERT outperform classical machine learning approaches. By providing insights into hate speech characteristics, this work enables improved content moderation and reduced exposure to online hate. The annotated dataset advances Arabic natural language processing research and resources.
Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistants (2024)

Recent works demonstrate that voice assistants do not perform equally well for everyone, but research on demographic robustness of speech technologies is still scarce. This is mainly due to the rarity of large datasets with controlled demographic tags. This paper introduces the Sonos Voice Control Bias Assessment Dataset, an open dataset composed of voice assistant requests for North American English in the music domain (1,038 speakers, 166 hours, 170k audio samples, with 9,040 unique labelled transcripts) with a controlled demographic diversity (gender, age, dialectal region and ethnicity). We also release a statistical demographic bias assessment methodology, at the univariate and multivariate levels, tailored to this specific use case and leveraging spoken language understanding metrics rather than transcription accuracy, which we believe is a better proxy for user experience. To demonstrate the capabilities of this dataset and statistical method to detect demographic bias, we consider a pair of state-of-the-art Automatic Speech Recognition and Spoken Language Understanding models. Results show statistically significant differences in performance across age, dialectal region and ethnicity. Multivariate tests are crucial to shed light on mixed effects between dialectal region, gender and age.
Source-free Domain Adaptation for Aspect-based Sentiment Analysis (2024)

Unsupervised Domain Adaptation (UDA) of the Aspect-based Sentiment Analysis (ABSA) task aims to transfer knowledge learned from labeled source domain datasets to unlabeled target domains on the assumption that samples from the source domain are freely accessible during the training period. However, this assumption can easily lead to privacy invasion issues in real-world applications, especially when the source data involves privacy-preserving domains such as healthcare and finance. In this paper, we introduce the Source-Free Domain Adaptation Framework for ABSA (SF-ABSA), which only allows model parameter transfer, not data transfer, between different domains. Specifically, the proposed SF-ABSA framework consists of two parts, i.e., feature-based adaptation and pseudo-label-based adaptation. Experiment results on four benchmarks show that the proposed framework performs competitively with traditional unsupervised domain adaptation methods under the premise of insufficient information, which demonstrates the superiority of our method under privacy conditions.
SPACE-IDEAS: A Dataset for Salient Information Detection in Space Innovation (2024)

Detecting salient parts in text using natural language processing has been widely used to mitigate the effects of information overflow. Nevertheless, most of the datasets available for this task are derived mainly from academic publications. We introduce SPACE-IDEAS, a dataset for salient information detection from innovation ideas related to the Space domain. The text in SPACE-IDEAS varies greatly and includes informal, technical, academic and business-oriented writing styles. In addition to a manually annotated dataset we release an extended version that is annotated using a large generative language model. We train different sentence and sequential sentence classifiers, and show that the automatically annotated dataset can be leveraged using multitask learning to train better classifiers.
Spanish Resource Grammar Version 2023 (2024)

We present the latest version of the Spanish Resource Grammar (SRG), a grammar of Spanish implemented in the HPSG formalism. Such grammars encode a complex set of hypotheses about syntax making them a resource for empirical testing of linguistic theory. They also encode a strict notion of grammaticality which makes them a resource for natural language processing applications in computer-assisted language learning. This version of the SRG uses the recent version of the Freeling morphological analyzer and is released along with an automatically created, manually verified treebank of 2,291 sentences. We explain the treebanking process, emphasizing how it is different from treebanking with manual annotation and how it contributes to empirically-driven development of syntactic theory. The treebanks' high level of consistency and detail makes them a resource for training high-quality semantic parsers and generally systems that benefit from precise and detailed semantics. Finally, we present the grammar`s coverage and overgeneration on 100 sentences from a learner corpus, a new research line related to developing methodologies for robust empirical evaluation of hypotheses in second language acquisition.
Spanless Event Annotation for Corpus-Wide Complex Event Understanding (2024)

We present a new approach to event annotation designed to promote whole-corpus understanding of complex events in multilingual, multimedia data as part of the DARPA Knowledge-directed Artificial Intelligence Reasoning Over Schemas (KAIROS) Program. KAIROS aims to build technology capable of reasoning about complex real-world events like a specific terrorist attack in order to provide actionable insights to end users. KAIROS systems extract events from a corpus, aggregate information into a coherent semantic representation, and instantiate observed events or predict unseen but expected events using a relevant event schema selected from a generalized schema library. To support development and testing for KAIROS Phase 2B we created a complex event annotation corpus that, instead of individual event mentions anchored in document spans with pre-defined event type labels, comprises a series of temporally ordered event frames populated with information aggregated from the whole corpus and labeled with an unconstrained tag set based on Wikidata Qnodes. The corpus makes a unique contribution to the resource landscape for information extraction, addressing gaps in the availability of multilingual, multimedia corpora for schema-based event representation. The corpus will be made available through publication in the Linguistic Data Consortium (LDC) catalog.
Sparse Logistic Regression with High-order Features for Automatic Grammar Rule Extraction from Treebanks (2024)

Descriptive grammars are highly valuable, but writing them is time-consuming and difficult. Furthermore, while linguists typically use corpora to create them, grammar descriptions often lack quantitative data. As for formal grammars, they can be challenging to interpret. In this paper, we propose a new method to extract and explore significant fine-grained grammar patterns and potential syntactic grammar rules from treebanks, in order to create an easy-to-understand corpus-based grammar. More specifically, we extract descriptions and rules across different languages for two linguistic phenomena, agreement and word order, using a large search space and paying special attention to the ranking order of the extracted rules. For that, we use a linear classifier to extract the most salient features that predict the linguistic phenomena under study. We associate statistical information to each rule, and we compare the ranking of the model`s results to those of other quantitative and statistical measures. Our method captures both well-known and less well-known significant grammar rules in Spanish, French, and Wolof.
Specifying Genericity through Inclusiveness and Abstractness Continuous Scales (2024)

This paper introduces a novel annotation framework for the fine-grained modeling of Noun Phrases' (NPs) genericity in natural language. The framework is designed to be simple and intuitive, making it accessible to non-expert annotators and suitable for crowd-sourced tasks. Drawing from theoretical and cognitive literature on genericity, this framework is grounded in established linguistic theory. Through a pilot study, we created a small but crucial annotated dataset of 324 sentences, serving as a foundation for future research. To validate our approach, we conducted an evaluation comparing our continuous annotations with existing binary annotations on the same dataset, demonstrating the framework`s effectiveness in capturing nuanced aspects of genericity. Our work offers a practical resource for linguists, providing a first annotated dataset and an annotation scheme designed to build real-language datasets that can be used in studies on the semantics of genericity, and NLP practitioners, contributing to the development of commonsense knowledge repositories valuable in enhancing various NLP applications.
SpeechAlign: A Framework for Speech Translation Alignment Evaluation (2024)

Speech-to-Speech and Speech-to-Text translation are currently dynamic areas of research. In our commitment to advance these fields, we present SpeechAlign, a framework designed to evaluate the underexplored field of source-target alignment in speech models. The SpeechAlign framework has two core components. First, to tackle the absence of suitable evaluation datasets, we introduce the Speech Gold Alignment dataset, built upon a English-German text translation gold alignment dataset. Secondly, we introduce two novel metrics, Speech Alignment Error Rate (SAER) and Time-weighted Speech Alignment Error Rate (TW-SAER), which enable the evaluation of alignment quality within speech models. While the former gives equal importance to each word, the latter assigns weights based on the length of the words in the speech signal. By publishing SpeechAlign we provide an accessible evaluation framework for model assessment, and we employ it to benchmark open-source Speech Translation models. In doing so, we contribute to the ongoing research progress within the fields of Speech-to-Speech and Speech-to-Text translation.
Speech Analysis of Language Varieties in Italy (2024)

Italy exhibits rich linguistic diversity across its territory due to the distinct regional languages spoken in different areas. Recent advances in self-supervised learning provide new opportunities to analyze Italy`s linguistic varieties using speech data alone. This includes the potential to leverage representations learned from large amounts of data to better examine nuances between closely related linguistic varieties. In this study, we focus on automatically identifying the geographic region of origin of speech samples drawn from Italy`s diverse language varieties. We leverage self-supervised learning models to tackle this task and analyze differences and similarities between Italy`s regional languages. In doing so, we also seek to uncover new insights into the relationships among these diverse yet closely related varieties, which may help linguists understand their interconnected evolution and regional development over time and space. To improve the discriminative ability of learned representations, we evaluate several supervised contrastive learning objectives, both as pre-training steps and additional fine-tuning objectives. Experimental evidence shows that pre-trained self-supervised models can effectively identify regions from speech recording. Additionally, incorporating contrastive objectives during fine-tuning improves classification accuracy and yields embeddings that distinctly separate regional varieties, demonstrating the value of combining self-supervised pre-training and contrastive learning for this task.
Speech Corpus for Korean Children with Autism Spectrum Disorder: Towards Automatic Assessment Systems (2024)

Despite the growing demand for digital therapeutics for children with Autism Spectrum Disorder (ASD), there is currently no speech corpus available for Korean children with ASD. This paper introduces a speech corpus specifically designed for Korean children with ASD, aiming to advance speech technologies such as pronunciation and severity evaluation. Speech recordings from speech and language evaluation sessions were transcribed, and annotated for articulatory and linguistic characteristics. Three speech and language pathologists rated these recordings for social communication severity (SCS) and pronunciation proficiency (PP) using a 3-point Likert scale. The total number of participants will be 300 for children with ASD and 50 for typically developing (TD) children. The paper also analyzes acoustic and linguistic features extracted from speech data collected and completed for annotation from 73 children with ASD and 9 TD children to investigate the characteristics of children with ASD and identify significant features that correlate with the clinical scores. The results reveal some speech and linguistic characteristics in children with ASD that differ from those in TD children or another subgroup of ASD categorized by clinical scores, demonstrating the potential for developing automatic assessment systems for SCS and PP.
Speech Recognition Corpus of the Khinalug Language for Documenting Endangered Languages (2024)

Automatic Speech Recognition (ASR) can be a valuable tool to document endangered languages. However, building ASR tools for these languages poses several difficult research challenges, notably data scarcity. In this paper, we show the whole process of creating a useful ASR tool for language documentation scenarios. We publish the first speech corpus for Khinalug, an endangered language spoken in Northern Azerbaijan. The corpus consists of 2.67 hours of labeled data from recordings of spontaneous speech about various topics. As Khinalug is an extremely low-resource language, we investigate the benefits of multilingual models for self-supervised learning and supervised learning and achieve the performance of 6.65 Character Error Rate (CER) points and 25.53 Word Error Rate (WER) points. The benefits of multilingual models are further validated through experimentation with three additional under-resourced languages. Lastly, this work conducts quality assessments with linguists on new recordings to investigate the model`s usefulness in language documentation. We observe an evident degradation for new recordings, indicating the importance of enhancing model robustness. In addition, we find the inaudible content is the main cause of wrong ASR predictions, suggesting relating work on incorporating contextual information.
SPICED: News Similarity Detection Dataset with Multiple Topics and Complexity Levels (2024)

The proliferation of news media outlets has increased the demand for intelligent systems capable of detecting redundant information in news articles in order to enhance user experience. However, the heterogeneous nature of news can lead to spurious findings in these systems: Simple heuristics such as whether a pair of news are both about politics can provide strong but deceptive downstream performance. Segmenting news similarity datasets into topics improves the training of these models by forcing them to learn how to distinguish salient characteristics under more narrow domains. However, this requires the existence of topic-specific datasets, which are currently lacking. In this article, we propose a novel dataset of similar news, SPICED, which includes seven topics: Crime {\&} Law, Culture {\&} Entertainment, Disasters {\&} Accidents, Economy {\&} Business, Politics {\&} Conflicts, Science {\&} Technology, and Sports. Futhermore, we present four different levels of complexity, specifically designed for news similarity detection task. We benchmarked the created datasets using MinHash, BERT, SBERT, and SimCSE models.
SPLICE: A Singleton-Enhanced PipeLIne for Coreference REsolution (2024)

Singleton mentions, i.e. entities mentioned only once in a text, are important to how humans understand discourse from a theoretical perspective. However previous attempts to incorporate their detection in end-to-end neural coreference resolution for English have been hampered by the lack of singleton mention spans in the OntoNotes benchmark. This paper addresses this limitation by combining predicted mentions from existing nested NER systems and features derived from OntoNotes syntax trees. With this approach, we create a near approximation of the OntoNotes dataset with all singleton mentions, achieving {\textasciitilde}94{\%} recall on a sample of gold singletons. We then propose a two-step neural mention and coreference resolution system, named SPLICE, and compare its performance to the end-to-end approach in two scenarios: the OntoNotes test set and the out-of-domain (OOD) OntoGUM corpus. Results indicate that reconstructed singleton training yields results comparable to end-to-end systems for OntoNotes, while improving OOD stability (+1.1 avg. F1). We conduct error analysis for mention detection and delve into its impact on coreference clustering, revealing that precision improvements deliver more substantial benefits than increases in recall for resolving coreference chains.