1,721,101 research outputs found
A Geometric Method for Detecting Semantic Coercion
No full textIn this paper we present state-of-the-art results on the computational classification of semantic type coercion, accomplished using a novel geometric method which is both context-sensitive and generalisable. We show that this method improves accuracy on a SemEval dataset over previous work, and gives promising results on a new more challenging experimental setup involving the same data. In addition to a description of our distributional semantic methodology and the results obtained on an established dataset, we offer an overview of the linguistic phenomenon of coercion and an analysis of the geometric features by which our results are achieved
Slovenian Emotion Dimension and Emotion Association Lexicon SloEmoLex 1.0
No full textSloEmoLex is a lexicon of emotion, valence, arousal and dominance for 19,998 Slovenian entries.
It includes and extends the Slovenian part of the LiLaH lexicon (Ljubešić et al., 2020; http://hdl.handle.net/11356/1318), in which words are annotated with binary values for association to one of the 8 basic emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, trust) and binary values for association with positive/negative sentiment.
SloEmoLex extends the LiLaH emotion lexicon with VAD scores from NRC VAD v1 (http://saifmohammad.com/WebPages/nrc-vad.html), and emotion intensity scores from NRC Emotion Intensity lexicon v1 (http://saifmohammad.com/WebPages/AffectIntensity.htm). Apart from the approx. 14,000 words present in Lilah, the lexicon includes 5,931 additional entries from the NRC VAD lexicon, some of which were translated with the use of sloWNet 3.1 (http://hdl.handle.net/11356/1026), and some entries (3,273) retained the machine translation provided in the Slovenian part of the NRC VAD lexicon.
If you use this work, please cite our paper:
Caporusso, Jaya, Hoogland, Damar, Brglez, Mojca, Kolosko, Boshko, Purver, Matthew, and Pollak, Senja, (2024). A Computational Analysis of the Dehumanisation of Migrants from Syria and Ukraine in Slovene News Media. THE 2024 JOINT INTERNATIONAL CONFERENCE ON COMPUTATIONAL LINGUISTICS, LANGUAGE RESOURCES AND EVALUATION (LREC-COLING 2024) 20-25 MAY, 2024, TORINO, ITALY
Let’s negotiate! A survey of negotiation dialogue systems
Full text linkNegotiation is a crucial ability in human communication. Recently, there has been a resurgent research interest in negotiation dialogue systems, whose goal is to create intelligent agents that can assist people in resolving conflicts or reaching agreements. Although there have been many explorations into negotiation dialogue systems, a systematic review of this task has not been performed to date. We aim to fill this gap by investigating recent studies in the field of negotiation dialogue systems, and covering benchmarks, evaluations and methodologies within the literature. We also discuss potential future directions, including multi-modal, multi-party and cross-cultural negotiation scenarios. Our goal is to provide the community with a systematic overview of negotiation dialogue systems and to inspire future research.</p
Analyzing the role of part-of-speech in code-switching:A corpus-based study
Full text linkCode-switching (CS) is a common linguistic phenomenon wherein speakers fluidly transition between languages in conversation. While the cognitive processes driving CS remain a complex domain, earlier investigations have shed light on its multifaceted triggers. This study delves into the influence of Part-of-Speech (POS) on the propensity of bilinguals to engage in CS, employing a comprehensive analysis of Spanish-English and Mandarin-English corpora. Compared with prior research, our findings not only affirm the existence of a statistically significant connection between POS and the likelihood of CS across language pairs, but notably find this relationship exhibits its maximum strength in proximity to CS instances, progressively diminishing as tokens distance themselves from these CS points
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Document Structure in Long Document Transformers
Full text linkLong documents often exhibit structure with hierarchically organized elements of different functions, such as section headers and paragraphs. Despite the omnipresence of document structure, its role in natural language processing (NLP) remains opaque. Do long-document Transformer models acquire an internal representation of document structure during pre-training? How can structural information be communicated to a model after pre-training, and how does it influence downstream performance? To answer these questions, we develop a novel suite of probing tasks to assess structure-awareness of long-document Transformers, propose general-purpose structure infusion methods, and evaluate the effects of structure infusion on QASPER and Evidence Inference, two challenging long-document NLP tasks. Results on LED and LongT5 suggest that they acquire implicit understanding of document structure during pre-training, which can be further enhanced by structure infusion, leading to improved end-task performance. To foster research on the role of document structure in NLP modeling, we make our data and code publicly available
The Role of Data Curation in Image Captioning
No full textImage captioning models are typically trained by treating all samples equally, neglecting to account for mismatched or otherwise difficult data points. In contrast, recent work has shown the effectiveness of training models by scheduling the data using curriculum learning strategies. This paper contributes to this direction by actively curating difficult samples in datasets without increasing the total number of samples. We explore the effect of using three data curation methods within the training process: complete removal of a sample, caption replacement, or image replacement via a text-to-image generation model. Experiments on the Flickr30K and COCO datasets with the BLIP and BEiT-3 models demonstrate that these curation methods do indeed yield improved image captioning models, underscoring their efficacy.</p
Sequence Shortening for Context-Aware Machine Translation
Full text linkContext-aware Machine Translation aims to improve translations of sentences by incorporating surrounding sentences as context. Towards this task, two main architectures have been applied, namely single-encoder (based on concatenation) and multi-encoder models. In this study, we show that a special case of multi-encoder architecture, where the latent representation of the source sentence is cached and reused as the context in the next step, achieves higher accuracy on the contrastive datasets (where the models have to rank the correct translation among the provided sentences) and comparable BLEU and COMET scores as the single- and multi-encoder approaches. Furthermore, we investigate the application of Sequence Shortening to the cached representations. We test three pooling-based shortening techniques and introduce two novel methods - Latent Grouping and Latent Selecting, where the network learns to group tokens or selects the tokens to be cached as context. Our experiments show that the two methods achieve competitive BLEU and COMET scores and accuracies on the contrastive datasets to the other tested methods while potentially allowing for higher interpretability and reducing the growth of memory requirements with increased context size
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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