12 papers
Synthetic Data Characterization via Training Dynamics
Irene Lago, Ana Ezquerro, David Vilares
Interpreting properties of LLM-generated data is important for understanding its utility and limitations across learning tasks. In this work, we characterize synthetic data through…
HEAD-QA v2: Expanding a Healthcare Benchmark for Reasoning
Alexis Correa-Guillén, Carlos Gómez-Rodríguez, David Vilares
We introduce HEAD-QA v2, an expanded and updated version of a Spanish/English healthcare multiple-choice reasoning dataset originally released by Vilares and Gómez-Rodríguez (2019)…
Hierarchical Bracketing Encodings Work for Dependency Graphs
Ana Ezquerro, Carlos Gómez-Rodríguez, David Vilares
We revisit hierarchical bracketing encodings from a practical perspective in the context of dependency graph parsing. The approach encodes graphs as sequences, enabling linear-time…
Bringing Emerging Architectures to Sequence Labeling in NLP
Ana Ezquerro, Carlos Gómez-Rodríguez, David Vilares
Pretrained Transformer encoders are the dominant approach to sequence labeling. While some alternative architectures-such as xLSTMs, structured state-space models, diffusion models…
Nested Named Entity Recognition as Single-Pass Sequence Labeling
Alberto Muñoz-Ortiz, David Vilares, Caio Corro +1
We cast nested named entity recognition (NNER) as a sequence labeling task by leveraging prior work that linearizes constituency structures, effectively reducing the complexity of…
Hierarchical Bracketing Encodings for Dependency Parsing as Tagging
Ana Ezquerro, David Vilares, Anssi Yli-Jyrä +1
We present a family of encodings for sequence labeling dependency parsing, based on the concept of hierarchical bracketing. We prove that the existing 4-bit projective encoding bel…
Better Benchmarking LLMs for Zero-Shot Dependency Parsing
Ana Ezquerro, Carlos Gómez-Rodríguez, David Vilares
While LLMs excel in zero-shot tasks, their performance in linguistic challenges like syntactic parsing has been less scrutinized. This paper studies state-of-the-art open-weight LL…
Dependency Graph Parsing as Sequence Labeling
Ana Ezquerro, David Vilares, Carlos Gómez-Rodríguez
Various linearizations have been proposed to cast syntactic dependency parsing as sequence labeling. However, these approaches do not support more complex graph-based representatio…
Dancing in the syntax forest: fast, accurate and explainable sentiment analysis with SALSA
Carlos Gómez-Rodríguez, Muhammad Imran, David Vilares +2
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 acc…
LyS at SemEval-2024 Task 3: An Early Prototype for End-to-End Multimodal Emotion Linking as Graph-Based Parsing
Ana Ezquerro, David Vilares
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-en…
From Partial to Strictly Incremental Constituent Parsing
Ana Ezquerro, Carlos Gómez-Rodríguez, David Vilares
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…
4 and 7-bit Labeling for Projective and Non-Projective Dependency Trees
Carlos Gómez-Rodríguez, Diego Roca, David Vilares
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 lab…