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Multimodal machine translation (MMT) is a challenging task that seeks to improve translation quality by incorporating visual information. However, recent studies have indicated that the visual information provided by existing MMT datasets is insufficient, causing models to disregard it and overestimate their capabiliti... | 3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset |
Background: Transformer-based language models have shown strong performance on many Natural Language Processing (NLP) tasks. Masked Language Models (MLMs) attract sustained interest because they can be adapted to different languages and sub-domains through training or fine-tuning on specific corpora while remaining lig... | A Benchmark Evaluation of Clinical Named Entity Recognition in French |
This paper introduces a novel benchmark that has been designed as a test bed for evaluating whether artificial agents are able to understand how to perform everyday activities, with a focus on the cooking domain. Understanding how to cook recipes is a highly challenging endeavour due to the underspecified and grounded ... | A Benchmark for Recipe Understanding in Artificial Agents |
Natural Language Processing (NLP) models tend to inherit and amplify stereotypical biases present in their training data, leading to harmful societal consequences. Current efforts to rectify these biases typically revolve around making models oblivious to bias, which is at odds with the idea that humans require increas... | ABLE: Agency-BeLiefs Embedding to Address Stereotypical Bias through Awareness Instead of Obliviousness |
This paper introduces a new task, abstractive multi-video captioning, which focuses on abstracting multiple videos with natural language. Unlike conventional video captioning tasks generating a specific caption for a video, our task generates an abstract caption of the shared content in a video group containing multipl... | Abstractive Multi-Video Captioning: Benchmark Dataset Construction and Extensive Evaluation |
Pre-trained Language Models have been shown to be able to emulate deductive reasoning in natural language. However, PLMs are easily affected by irrelevant information (e.g., entity) in instance-level proofs when learning deductive reasoning. To address this limitation, we propose an Abstract-level Deductive Reasoner (A... | Abstract-level Deductive Reasoning for Pre-trained Language Models |
Text generation with beam search has proven successful in a wide range of applications. We point out that, though largely overlooked in the literature, the commonly-used implementation of beam decoding (e.g., Hugging Face Transformers and fairseq) uses a first come, first served heuristic: it keeps a set of already com... | A Call for Clarity in Beam Search: How It Works and When It Stops |
This paper proposes a canonical form for Multiword Expressions (MWEs), in particular for the Dutch language. The canonical form can be enriched with all kinds of annotations that can be used to describe the properties of the MWE and its components. It also introduces the DUCAME (DUtch CAnonical Multiword Expressions) l... | A Canonical Form for Flexible Multiword Expressions |
Empowered by the large-scale pretrained language models, existing dialogue systems have demonstrated impressive performance conducting fluent and natural-sounding conversations. However, they are still plagued by the <b>hallucination</b> problem, causing unpredictable factual errors in the generated responses. Recently... | A Cause-Effect Look at Alleviating Hallucination of Knowledge-grounded Dialogue Generation |
This paper introduces the licence-based access control framework developed by the Language Data Commons of Australia (LDaCA) for a range of language collections, with examples given of implementation for significant Indigenous and Australian English collections. Language collections may be curated for many reasons, suc... | Access Control Framework for Language Collections |
Previous stance detection studies typically concentrate on evaluating stances within individual instances, thereby exhibiting limitations in effectively modeling multi-party discussions concerning the same specific topic, as naturally transpire in authentic social media interactions. This constraint arises primarily du... | A Challenge Dataset and Effective Models for Conversational Stance Detection |
We study in this paper the problem of clustering comparable corpora, building upon the observation that different types of clusters can be present in such corpora: monolingual clusters comprising documents in a single language, and bilingual or multilingual clusters comprising documents written in different languages. ... | A Closer Look at Clustering Bilingual Comparable Corpora |
Empathy is critical for effective communication and mental health support, and in many online health communities people anonymously engage in conversations to seek and provide empathetic support. The ability to automatically recognize and detect empathy contributes to the understanding of human emotions expressed in te... | AcnEmpathize: A Dataset for Understanding Empathy in Dermatology Conversations |
Automatic measures of similarity between sentences or utterances are invaluable for training speech synthesizers, evaluating machine translation, and assessing learner productions. While there exist measures for semantic similarity and prosodic similarity, there are as yet none for pragmatic similarity. To enable the t... | A Collection of Pragmatic-Similarity Judgments over Spoken Dialog Utterances |
Data-to-text systems offer a transformative approach to generating textual content in data-rich environments. This paper describes the architecture and deployment of Prosebot, a community-driven data-to-text platform tailored for generating textual summaries of football matches derived from match statistics. The system... | A Community-Driven Data-to-Text Platform for Football Match Summaries |
The application of Differential Privacy to Natural Language Processing techniques has emerged in relevance in recent years, with an increasing number of studies published in established NLP outlets. In particular, the adaptation of Differential Privacy for use in NLP tasks has first focused on the *word-level*, where c... | A Comparative Analysis of Word-Level Metric Differential Privacy: Benchmarking the Privacy-Utility Trade-off |
While extensive work has examined the explicit and implicit biases in large language models (LLMs), little research explores the relation between these two types of biases. This paper presents a comparative study of the explicit and implicit biases in LLMs grounded in social psychology. Social psychology distinguishes ... | A Comparative Study of Explicit and Implicit Gender Biases in Large Language Models via Self-evaluation |
Dehumanisation involves the perception and/or treatment of a social group’s members as less than human. This phenomenon is rarely addressed with computational linguistic techniques. We adapt a recently proposed approach for English, making it easier to transfer to other languages and to evaluate, introducing a new sent... | A Computational Analysis of the Dehumanisation of Migrants from Syria and Ukraine in Slovene News Media |
This paper explores grammaticization of deverbal prepositions by a computational approach based on corpus data. Deverbal prepositions are words or phrases that are derived from a verb and that behave as a preposition such as “regarding” and “according to”. Linguistic studies have revealed important aspects of grammatic... | A Computational Approach to Quantifying Grammaticization of English Deverbal Prepositions |
In this paper we describe a computational model of Latvian morphology that provides a formal structure for Latvian word form inflection and has been implemented in software for generation, analysis and lemmatization of Latvian word forms. The work was motivated by the need for a NLP inflection model that can cover all ... | A Computational Model of Latvian Morphology |
Pictographs have been found to improve patient comprehension of medical information or instructions. However, tools to produce pictograph representations from natural language are still scarce. In this contribution we describe a system that automatically translates French speech into pictographs to enable diagnostic in... | A Concept Based Approach for Translation of Medical Dialogues into Pictographs |
Large Language Models (LLMs) have been developed without a theoretical framework, yet we posit that evaluating and improving LLMs will benefit from the development of theoretical frameworks that enable comparison of the structures of human language and the model of language built up by LLMs through the processing of te... | A Construction Grammar Corpus of Varying Schematicity: A Dataset for the Evaluation of Abstractions in Language Models |
All state-of-the-art coreference resolution (CR) models involve finetuning a pretrained language model. Whether the superior performance of one CR model over another is due to the choice of language model or other factors, such as the task-specific architecture, is difficult or impossible to determine due to lack of a ... | A Controlled Reevaluation of Coreference Resolution Models |
Manufacturing specifications are documents entailing different techniques, processes, and components involved in manufacturing. There is a growing demand for named entity recognition (NER) resources and techniques for manufacturing-specific named entities, with the development of smart manufacturing. In this paper, we ... | A Corpus and Method for Chinese Named Entity Recognition in Manufacturing |
We develop novel annotation guidelines for sentence-level subjectivity detection, which are not limited to language-specific cues. We use our guidelines to collect NewsSD-ENG, a corpus of 638 objective and 411 subjective sentences extracted from English news articles on controversial topics. Our corpus paves the way fo... | A Corpus for Sentence-Level Subjectivity Detection on English News Articles |
We present the first comprehensive set of guidelines for German Abstract Meaning Representation (Deutsche AMR, DeAMR) along with an annotated corpus of 400 DeAMR. Taking English AMR (EnAMR) as our starting point, we propose significant adaptations to faithfully represent the structure and semantics of German, focusing ... | A Corpus of German Abstract Meaning Representation (DeAMR) |
When assessing second language proficiency (L2), evaluation of spontaneous speech performance is crucial. This paper presents a corpus of spontaneous L2 English speech, focusing on the speech performance of B1 and B2 proficiency speakers. Two hundred and sixty university students were recorded during a speaking task as... | A Corpus of Spontaneous L2 English Speech for Real-situation Speaking Assessment |
Sarcasm primarily involves saying something but “meaning the opposite” or “meaning something completely different” in order to convey a particular tone or mood. In both the above cases, the “meaning” is reflected by the communicative intention of the speaker, known as dialogue acts. In this paper, we seek to investigat... | Action and Reaction Go Hand in Hand! a Multi-modal Dialogue Act Aided Sarcasm Identification |
Sign language is the primary communication medium for people who are deaf or have hearing loss. However, given the divergent range of sensory abilities of these individuals, there is a communication gap that needs to be addressed. In this paper, we present action-concentrated embedding (ACE), which is a novel sign toke... | Action-Concentrated Embedding Framework: This Is Your Captain Sign-tokening |
We explore multiple important choices that have not been analyzed in conjunction regarding active learning for token classification using transformer networks. These choices are: (i) how to select what to annotate, (ii) decide whether to annotate entire sentences or smaller sentence fragments, (iii) how to train with i... | Active Learning Design Choices for NER with Transformers |
We present and describe two language resources in this paper: CATalog 1.0, the largest text corpus in Catalan to date, and CURATE (Corpus Utility for RAting TExt), a modular, parallelizable pipeline used for processing and scoring documents based on text quality that we have optimised to run in High Performance Cluster... | A CURATEd CATalog: Rethinking the Extraction of Pretraining Corpora for Mid-Resourced Languages |
The fine-tuning paradigm has been widely adopted to train neural models tailored for specific tasks. However, the recent upsurge of Large Language Models (LLMs), characterized by billions of parameters, has introduced profound computational challenges to the fine-tuning process. This has fueled intensive research on Pa... | AdaKron: An Adapter-based Parameter Efficient Model Tuning with Kronecker Product |
Sentence representation learning is a fundamental task in NLP. Existing methods use contrastive learning (CL) to learn effective sentence representations, which benefit from high-quality contrastive data but require extensive human annotation. Large language models (LLMs) like ChatGPT and GPT4 can automatically generat... | Adaptive Reinforcement Tuning Language Models as Hard Data Generators for Sentence Representation |
Traditional non-simultaneous Sign Language Translation (SLT) methods, while effective for pre-recorded videos, face challenges in real-time scenarios due to inherent inference delays. The emerging field of simultaneous SLT aims to address this issue by progressively translating incrementally received sign video. Howeve... | Adaptive Simultaneous Sign Language Translation with Confident Translation Length Estimation |
In this study, we present a novel historical Chinese dataset for named entity recognition, entity linking, coreference and entity relations. We use data from Chinese newspapers from 1872 to 1949 and multilingual bibliographic resources from the same period. The period and the language are the main strength of the prese... | A Dataset for Named Entity Recognition and Entity Linking in Chinese Historical Newspapers |
User-generated data sources have gained significance in uncovering Adverse Drug Reactions (ADRs), with an increasing number of discussions occurring in the digital world. However, the existing clinical corpora predominantly revolve around scientific articles in English. This work presents a multilingual corpus of texts... | A Dataset for Pharmacovigilance in German, French, and Japanese: Annotating Adverse Drug Reactions across Languages |
In the realm of conversational dynamics, individual idiosyncrasies challenge the suitability of a one-size-fits-all approach for dialogue agent responses. Prior studies often assumed the speaker’s persona’s immediate availability, a premise not universally applicable. To address this gap, we explore the Speaker Profili... | Adding SPICE to Life: Speaker Profiling in Multiparty Conversations |
Introducing ADEA: a German dataset that captures online dialogues and focuses on ethical issues related to future AI applications. This dataset, which includes over 2800 labeled user utterances on four different topics, is specifically designed for the training of chatbots that can navigate the complexities of real-wor... | ADEA: An Argumentative Dialogue Dataset on Ethical Issues Concerning Future A.I. Applications |
The proliferation of open knowledge graphs has led to a surge in scholarly research on the topic over the past decade. This paper presents a bibliometric analysis of the scholarly literature on open knowledge graphs published between 2013 and 2023. The study aims to identify the trends, patterns, and impact of research... | A Decade of Scholarly Research on Open Knowledge Graphs |
Integer Linear Programming (ILP) has been proposed as a formalism for encoding precise structural and semantic constraints for Natural Language Inference (NLI). However, traditional ILP frameworks are non-differentiable, posing critical challenges for the integration of continuous language representations based on deep... | A Differentiable Integer Linear Programming Solver for Explanation-Based Natural Language Inference |
Document-level text simplification, a task that combines single-document summarization and intra-sentence simplification, has garnered significant attention. However, studies have primarily focused on languages such as English and German, leaving Japanese and similar languages underexplored because of a scarcity of lin... | A Document-Level Text Simplification Dataset for Japanese |
Radiology report analysis provides valuable information that can aid with public health initiatives, and has been attracting increasing attention from the research community. In this work, we present a novel insight that the structure of a radiology report (namely, the Findings and Impression sections) offers different... | A Dual-View Approach to Classifying Radiology Reports by Co-Training |
Semi-supervised learning that leverages synthetic data for training has been widely adopted for developing automatic post-editing (APE) models due to the lack of training data. With this aim, we focus on data-synthesis methods to create high-quality synthetic data. Given that APE takes as input a machine-translation re... | Advancing Semi-Supervised Learning for Automatic Post-Editing: Data-Synthesis by Mask-Infilling with Erroneous Terms |
Topic segmentation and outline generation strive to divide a document into coherent topic sections and generate corresponding subheadings, unveiling the discourse topic structure of a document. Compared with sentence-level topic structure, the paragraph-level topic structure can quickly grasp and understand the overall... | Advancing Topic Segmentation and Outline Generation in Chinese Texts: The Paragraph-level Topic Representation, Corpus, and Benchmark |
Transformer language models (LMs) are fundamental to NLP research methodologies and applications in various languages. However, developing such models specifically for the Russian language has received little attention. This paper introduces a collection of 13 Russian Transformer LMs, which spans encoder (ruBERT, ruRoB... | A Family of Pretrained Transformer Language Models for Russian |
With an auxiliary corpus (non-target speaker corpus) for model pre-training, Text-to-Speech (TTS) methods can generate high-quality speech with a limited target speaker corpus. However, this approach comes with expensive training costs. To overcome the challenge, a high-quality TTS method is proposed, significantly red... | A Fast and High-quality Text-to-Speech Method with Compressed Auxiliary Corpus and Limited Target Speaker Corpus |
We introduce a frustratingly simple, highly efficient, and surprisingly effective decoding method, termed Frustratingly Simple Decoding (FSD), for neural text generation. The idea behind FSD is straightforward: We construct an anti-language model (anti-LM) based on previously generated text, which is employed to penali... | A Frustratingly Simple Decoding Method for Neural Text Generation |
Situated conversations, which refer to visual information as visual question answering (VQA), often contain ambiguities caused by reliance on directive information. This problem is exacerbated because some languages, such as Japanese, often omit subjective or objective terms. Such ambiguities in questions are often cla... | A Gaze-grounded Visual Question Answering Dataset for Clarifying Ambiguous Japanese Questions |
This paper introduces a novel problem of automated question generation for courtroom examinations, CourtQG. While question generation has been studied in domains such as educational testing and product description, CourtQG poses several unique challenges owing to its non-cooperative and agenda-driven nature. Specifical... | Agenda-Driven Question Generation: A Case Study in the Courtroom Domain |
In this work, we introduce a generative model, PLC+, for generating Lambek Categorial Grammar(LCG) sequents. We also introduce a simple method to numerically estimate the model’s parameters from an annotated corpus. Then we compare our model with probabilistic context-free grammars (PCFGs) and show that PLC+ simultaneo... | A Generative Model for Lambek Categorial Sequents |
Language change has been the subject of numerous studies in linguistics. However, due to the dynamic and complex nature of this phenomenon, and to the difficulty of obtaining extensive real data of language in use, some of its aspects remain obscure. In recent years, nonetheless, research has used computational modelin... | Agent-based Modeling of Language Change in a Small-world Network |
In this paper we present a series of experiments towards POS tagging Corsican, a less-resourced language spoken in Corsica and linguistically related to Italian. The first contribution is Corsican-POS, the first gold standard POS-tagged corpus for Corsica, composed of 500 sentences manually annotated with the Universal... | Agettivu, Aggitivu o Aghjettivu? POS Tagging Corsican Dialects |
Recent advancements in Chain-of-Thought prompting have facilitated significant breakthroughs for Large Language Models (LLMs) in complex reasoning tasks. Current research enhances the reasoning performance of LLMs by sampling multiple reasoning chains and ensembling based on the answer frequency. However, this approach... | Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models |
Aspect category sentiment analysis (ACSA) aims to simultaneously detect aspect categories and their corresponding sentiment polarities (category-sentiment pairs). Some recent studies have used pre-trained generative models to complete ACSA and achieved good results. However, for ACSA, generative models still face three... | A Hierarchical Sequence-to-Set Model with Coverage Mechanism for Aspect Category Sentiment Analysis |
This paper introduces TVB-HKSL-News, a new Hong Kong sign language (HKSL) dataset collected from a TV news program over a period of 7 months. The dataset is collected to enrich resources for HKSL and support research in large-vocabulary continuous sign language recognition (SLR) and translation (SLT). It consists of 16... | A Hong Kong Sign Language Corpus Collected from Sign-interpreted TV News |
Aspect-Based Sentiment Analysis ( ABSA) aims to identify terms or multiword expressions (MWEs) on which sentiments are expressed and the sentiment polarities associated with them. The development of supervised models has been at the forefront of research in this area. However, training these models requires the availab... | A Hybrid Approach to Aspect Based Sentiment Analysis Using Transfer Learning |
Text Simplification enhances the readability of texts for specific audiences. However, automated models may introduce unwanted content or omit essential details, necessitating a focus on maintaining faithfulness to the original input. Furthermore, existing simplified corpora contain instances of low faithfulness. Motiv... | A Japanese News Simplification Corpus with Faithfulness |
Knowledge-based, open-domain dialogue generation aims to build chit-chat systems that talk to humans using mined support knowledge. Many types and sources of knowledge have previously been shown to be useful as support knowledge. Even in the era of large language models, response generation grounded in knowledge retrie... | A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation |
This paper introduces a large annotated corpus of public domain German poetry, covering the time period from 1600 to the 1920s with 65k poems. We describe how the corpus was compiled, how it was cleaned (including duplicate detection), and how it looks now in terms of size, format, temporal distribution, and automatic ... | A Large Annotated Reference Corpus of New High German Poetry |
Cross-lingual pre-training methods mask and predict tokens in multilingual text to generalize diverse multilingual information. However, due to the lack of sufficient aligned multilingual resources in the pre-training process, these methods may not fully explore the multilingual correlation of masked tokens, resulting ... | A Lifelong Multilingual Multi-granularity Semantic Alignment Approach via Maximum Co-occurrence Probability |
Preparing historical newspaper collections is a complicated endeavour, consisting of multiple steps that have to be carefully adapted to the specific content in question, including imaging, layout prediction, optical character recognition, and linguistic annotation. To address the high costs associated with the process... | A Lightweight Approach to a Giga-Corpus of Historical Periodicals: The Story of a Slovenian Historical Newspaper Collection |
This paper presents a merged collection of entity and coreference annotated data grounded in the Universal Dependencies (UD) treebanks for the two written forms of Norwegian: Bokmål and Nynorsk. The aligned and converted corpora are the Norwegian Named Entities (NorNE) and Norwegian Anaphora Resolution Corpus (NARC). W... | Aligning the Norwegian UD Treebank with Entity and Coreference Information |
The visual question localized-answering (VQLA) system has garnered increasing attention due to its potential as a knowledgeable assistant in surgical education. Apart from providing text-based answers, VQLA can also pinpoint the specific region of interest for better surgical scene understanding. Although recent Transf... | Alignment before Awareness: Towards Visual Question Localized-Answering in Robotic Surgery via Optimal Transport and Answer Semantics |
The advent of scalable deep models and large datasets has improved the performance of Neural Machine Translation (NMT). Knowledge Distillation (KD) enhances efficiency by transferring knowledge from a teacher model to a more compact student model. However, KD approaches to Transformer architecture often rely on heurist... | Align-to-Distill: Trainable Attention Alignment for Knowledge Distillation in Neural Machine Translation |
Semantic role labeling is an essential component of semantic and syntactic processing of natural languages, which reveals the predicate-argument structure of the language. Despite its importance, semantic role labeling for the Korean language has not been studied extensively. One notable issue is the lack of uniformity... | A Linguistically-Informed Annotation Strategy for Korean Semantic Role Labeling |
Abstractive summarization commonly suffers from exposure bias caused by supervised teacher-force learning, that a model predicts the next token conditioned on the accurate pre-context during training while on its preceding outputs at inference. Existing solutions bridge this gap through un- or semi-supervised holistic ... | Alleviating Exposure Bias in Abstractive Summarization via Sequentially Generating and Revising |
This paper presents ALLIES, a meta corpus which gathers and extends existing French corpora collected from radio and TV shows. The corpus contains 1048 audio files for about 500 hours of speech. Agglomeration of data is always a difficult issue, as the guidelines used to collect, annotate and transcribe speech are gene... | ALLIES: A Speech Corpus for Segmentation, Speaker Diarization, Speech Recognition and Speaker Change Detection |
Generating coherent and credible explanations remains a significant challenge in the field of AI. In recent years, researchers have delved into the utilization of entailment trees to depict explanations, which exhibit a reasoning process of how a hypothesis is deduced from the supporting facts. However, existing models... | A Logical Pattern Memory Pre-trained Model for Entailment Tree Generation |
The task of financial analysis primarily encompasses two key areas: stock trend prediction and the corresponding financial question answering. Currently, machine learning and deep learning algorithms (ML&DL) have been widely applied for stock trend predictions, leading to significant progress. However, these methods fa... | AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework |
According to the United Nations Development Programme, gender inequality is a metric that is composed of three dimensions: reproductive health, empowerment, and the labour market. Gender inequality is an obstacle to equal opportunities in society as a whole. In this paper we present our work-in-progress of designing an... | A Luxembourgish Corpus as a Gender Bias Evaluation Testset |
Studies on literary quality have constantly stimulated the interest of critics, both in theoretical and empirical fields. To examine the perceived quality of literary works, some approaches have focused on data annotated through crowd-sourcing platforms, and others relied on available expert annotated data. In this wor... | A Matter of Perspective: Building a Multi-Perspective Annotated Dataset for the Study of Literary Quality |
Contextualised Language Models (LM) improve on traditional word embeddings by encoding the meaning of words in context. However, such models have also made it possible to learn high-quality decontextualised concept embeddings. Three main strategies for learning such embeddings have thus far been considered: (i) fine-tu... | AMenDeD: Modelling Concepts by Aligning Mentions, Definitions and Decontextualised Embeddings |
We present a corpus of 100 documents, named OBSINFOX, selected from 17 sources of French press considered unreliable by expert agencies, annotated using 11 labels by 8 annotators. By collecting more labels than usual, by more annotators than is typically done, we can identify features that humans consider as characteri... | A Multi-Label Dataset of French Fake News: Human and Machine Insights |
In this paper, we explore physical commonsense reasoning of large language models (LLMs) and propose a specific methodology to evaluate low-level understanding of the physical world. Specifically, the goal is to create a test set to analyze physical commonsense reasoning in large language models for Italian and focus o... | A Multi-layered Approach to Physical Commonsense Understanding: Creation and Evaluation of an Italian Dataset |
We report the creation of the first high-quality corpus of Aromanian - an endangered Romance language spoken in the Balkans - and the equivalent sentence-aligned translations into Romanian, English, and French. The corpus is released publicly using several orthographic standards and consists in short stories collected ... | A Multilingual Parallel Corpus for Aromanian |
The automatic translation of spoken language into pictogram units can facilitate communication involving individuals with language impairments. However, there is no established translation formalism or publicly available datasets for training end-to-end speech translation systems. This paper introduces the first aligne... | A Multimodal French Corpus of Aligned Speech, Text, and Pictogram Sequences for Speech-to-Pictogram Machine Translation |
In this paper, we propose a new setting for generating product descriptions from images, augmented by marketing keywords. It leverages the combined power of visual and textual information to create descriptions that are more tailored to the unique features of products. For this setting, previous methods utilize visual ... | A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation |
Precise understanding of free-text radiology reports through localised extraction of clinical findings can enhance medical imaging applications like computer-aided diagnosis. We present a new task, that of segmenting radiology reports into topically meaningful passages (segments) and a transformer-based model that both... | A Multi-Task Transformer Model for Fine-grained Labelling of Chest X-Ray Reports |
Clarifying the effects of subjective ideas on group performance is essential for future dialogue systems to improve mutual understanding among humans and group creativity. However, there has been little focus on dialogue research on quantitatively analyzing the effects of the quality and quantity of subjective informat... | Analysis of Sensation-transfer Dialogues in Motorsports |
In this paper, we investigate how new bilingual vocabulary is acquired through Iterative Back-Translation (IBT), which is known as a data augmentation method for machine translation from monolingual data of both source and target languages. To reveal the acquisition process, we first identify the word translation pairs... | Analysis on Unsupervised Acquisition Process of Bilingual Vocabulary through Iterative Back-Translation |
Chain-of-Thought (CoT) prompting combined with large language models (LLM) has shown great potential in improving performance on challenging reasoning tasks. While understanding why CoT prompting is effective is crucial for the application and improvement of CoT prompting, few studies have addressed this issue. Besides... | Analyzing Chain-of-thought Prompting in Black-Box Large Language Models via Estimated V-information |
Pre-training and fine-tuning large language models (LMs) is currently the state-of-the-art methodology for enabling data-scarce downstream tasks. However, the derived models still tend to replicate and perpetuate social biases. To understand this process in more detail, this paper investigates the actual effects of lea... | Analyzing Effects of Learning Downstream Tasks on Moral Bias in Large Language Models |
Word Sense Disambiguation (WSD) is a key task in Natural Language Processing (NLP), aiming to assign the correct meaning (sense) to a word in context. However, traditional WSD systems rely on WordNet as the underlying sense inventory, often differentiating meticulously between subtle nuances of word meanings, which may... | Analyzing Homonymy Disambiguation Capabilities of Pretrained Language Models |
Interpretation methods provide saliency scores indicating the importance of input words for neural summarization models. Prior work has analyzed models by comparing them to human behavior, often using eye-gaze as a proxy for human attention in reading tasks such as classification. This paper presents a framework to ana... | Analyzing Interpretability of Summarization Model with Eye-gaze Information |
In this paper, we investigate and evaluate large language models’ capability in location prediction. We present experimental results with four models—FLAN-T5, FLAN-UL2, FLAN-Alpaca, and ChatGPT—in various instruction finetuning and exemplar settings. We analyze whether taking into account the context—tweets published b... | Analyzing Large Language Models’ Capability in Location Prediction |
Recent advances in large language models (LLMs) have enabled users to generate fluent and seemingly convincing text. However, these models have uneven performance in different languages, which is also associated with undesirable societal biases toward marginalized populations. Specifically, there is relatively little w... | Analyzing Occupational Distribution Representation in Japanese Language Models |
The ever-growing number of people suffering from mental distress has motivated significant research initiatives towards automated depression estimation. Despite the multidisciplinary nature of the task, very few of these approaches include medical professionals in their research process, thus ignoring a vital source of... | Analyzing Symptom-based Depression Level Estimation through the Prism of Psychiatric Expertise |
The discourse surrounding climate change on social media platforms has emerged as a significant avenue for understanding public sentiments, perspectives, and engagement with this critical global issue. The unavailability of publicly available datasets, coupled with ignoring the multi-aspect analysis of climate discours... | Analyzing the Dynamics of Climate Change Discourse on Twitter: A New Annotated Corpus and Multi-Aspect Classification |
Large language models (LLMs) such as Llama 2 perform very well on tasks that involve both natural language and source code, particularly code summarization and code generation. We show that for the task of code summarization, the performance of these models on individual examples often depends on the amount of (subword... | Analyzing the Performance of Large Language Models on Code Summarization |
We empirically study the ability of a Large Language Model (gpt-3.5-turbo-instruct) to understand morphologically complex words. In our experiments, we looked at a variety of tasks to analyse German compounds with regard to compositional word formation and derivation, such as identifying the head noun of existing and n... | Analyzing the Understanding of Morphologically Complex Words in Large Language Models |
It is well known that left conjuncts tend to be shorter in English coordinate structures. On the basis of Penn Treebank, Przepiórkowski and Woźniak 2023 (in ACL 2023 proceedings) show that this tendency depends on the difference between lengths of conjuncts: the larger the difference, the stronger the tendency for the ... | An Argument for Symmetric Coordination from Dependency Length Minimization: A Replication Study |
Detecting synthetically generated text in the wild has become increasingly difficult with advances in Natural Language Generation techniques and the proliferation of freely available Large Language Models (LLMs). Social media and news sites can be flooded with synthetically generated misinformation via tweets and posts... | A Natural Approach for Synthetic Short-Form Text Analysis |
Data availability is crucial for advancing artificial intelligence applications, including voice-based technologies. As content creation, particularly in social media, experiences increasing demand, translation and text-to-speech (TTS) technologies have become essential tools. Notably, the performance of these TTS tech... | An Automated End-to-End Open-Source Software for High-Quality Text-to-Speech Dataset Generation |
In this paper, we present AnCast, an intuitive and efficient tool for evaluating graph-based meaning representations (MR). AnCast implements evaluation metrics that are well understood in the NLP community, and they include concept F1, unlabeled relation F1, labeled relation F1, and weighted relation F1. The efficiency... | Anchor and Broadcast: An Efficient Concept Alignment Approach for Evaluation of Semantic Graphs |
With the increasing availability of multimodal content on social media, consisting primarily of text and images, multimodal named entity recognition (MNER) has gained a wide-spread attention. A fundamental challenge of MNER lies in effectively aligning different modalities. However, the majority of current approaches r... | An Effective Span-based Multimodal Named Entity Recognition with Consistent Cross-Modal Alignment |
Implicit Discourse Relation Recognition (IDRR), which is the task of recognizing the semantic relation between given text spans that do not contain overt clues, is a long-standing and challenging problem. In particular, the paucity of training data for some error-prone discourse relations makes the problem even more ch... | An Empirical Study of Synthetic Data Generation for Implicit Discourse Relation Recognition |
Massively multilingual neural machine translation (MMNMT) has been proven to enhance the translation quality of low-resource languages. In this paper, we empirically investigate the translation robustness of Indonesian-Chinese translation in the face of various naturally occurring noise. To assess this, we create a rob... | An Empirical Study on the Robustness of Massively Multilingual Neural Machine Translation |
To assist in the documentation of Čakavian, an endangered language variety closely related to Croatian, we test four currently available ASR models that are trained with Croatian data and assess their performance in the transcription of Čakavian audio data. We compare the models’ word error rates, analyze the word-leve... | An Evaluation of Croatian ASR Models for Čakavian Transcription |
Time-Sensitive Question Answering (TSQA) is to answer questions qualified for a certain timestamp based on the given document. It is split into easy and hard modes depending on whether the document contain time qualifiers mentioned in the question. While existing models have performed well on easy mode, their performan... | An Event-based Abductive Learning for Hard Time-sensitive Question Answering |
We present the HPLT (High Performance Language Technologies) language resources, a new massive multilingual dataset including both monolingual and bilingual corpora extracted from CommonCrawl and previously unused web crawls from the Internet Archive. We describe our methods for data acquisition, management and process... | A New Massive Multilingual Dataset for High-Performance Language Technologies |
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