Zobrazeno 1 - 10
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pro vyhledávání: '"Vilares, David"'
Sentiment analysis is a key technology for companies and institutions to gauge public opinion on products, services or events. However, for large-scale sentiment analysis to be accessible to entities with modest computational resources, it needs to b
Externí odkaz:
http://arxiv.org/abs/2406.16071
Autor:
Ezquerro, Ana, Vilares, David
This paper describes our participation in SemEval 2024 Task 3, which focused on Multimodal Emotion Cause Analysis in Conversations. We developed an early prototype for an end-to-end system that uses graph-based methods from dependency parsing to iden
Externí odkaz:
http://arxiv.org/abs/2405.06483
We study incremental constituent parsers to assess their capacity to output trees based on prefix representations alone. Guided by strictly left-to-right generative language models and tree-decoding modules, we build parsers that adhere to a strong d
Externí odkaz:
http://arxiv.org/abs/2402.02782
We introduce an encoding for parsing as sequence labeling that can represent any projective dependency tree as a sequence of 4-bit labels, one per word. The bits in each word's label represent (1) whether it is a right or left dependent, (2) whether
Externí odkaz:
http://arxiv.org/abs/2310.14319
Since the popularization of BiLSTMs and Transformer-based bidirectional encoders, state-of-the-art syntactic parsers have lacked incrementality, requiring access to the whole sentence and deviating from human language processing. This paper explores
Externí odkaz:
http://arxiv.org/abs/2309.16254
We present an approach for assessing how multilingual large language models (LLMs) learn syntax in terms of multi-formalism syntactic structures. We aim to recover constituent and dependency structures by casting parsing as sequence labeling. To do s
Externí odkaz:
http://arxiv.org/abs/2309.11165
Publikováno v:
Artificial Intelligence Review 57, 265 (2024)
We conduct a quantitative analysis contrasting human-written English news text with comparable large language model (LLM) output from six different LLMs that cover three different families and four sizes in total. Our analysis spans several measurabl
Externí odkaz:
http://arxiv.org/abs/2308.09067
Autor:
Muñoz-Ortiz, Alberto, Vilares, David
The usefulness of part-of-speech tags for parsing has been heavily questioned due to the success of word-contextualized parsers. Yet, most studies are limited to coarse-grained tags and high quality written content; while we know little about their i
Externí odkaz:
http://arxiv.org/abs/2305.15119
PoS tags, once taken for granted as a useful resource for syntactic parsing, have become more situational with the popularization of deep learning. Recent work on the impact of PoS tags on graph- and transition-based parsers suggests that they are on
Externí odkaz:
http://arxiv.org/abs/2210.15219
Treebank selection for parsing evaluation and the spurious effects that might arise from a biased choice have not been explored in detail. This paper studies how evaluating on a single subset of treebanks can lead to weak conclusions. First, we take
Externí odkaz:
http://arxiv.org/abs/2209.06699