Papers by Marco Antonio Sobrevilla Cabezudo

4 papers
Natural Language Generation: Recently Learned Lessons, Directions for Semantic Representation-based Approaches, and the Case of Brazilian Portuguese Language (P19-2)

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Challenge: Natural Language Generation (NLG) is a promising area in Natural Language Processing (NLP) .
Approach: They present a review of the literature on Natural Language Generation in Brazilian Portuguese.
Outcome: The proposed approaches are based on the Abstract Meaning Representation formalism and have potential future directions.
The Dangers of trusting Stochastic Parrots: Faithfulness and Trust in Open-domain Conversational Question Answering (2023.findings-acl)

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Challenge: Empirical studies of dialogue have shown that people use different kinds of context-dependent linguistic behavior to indicate grounding, including use of fragments, ellipsis and pronominal reference.
Approach: They propose to use open-domain question answering systems as test-bed for task based dialog generation and compare open- and closed-book models to test their hypothesis.
Outcome: The proposed model parrots user input while providing an unfaithful response.
Efficient Strategies for Hierarchical Text Classification: External Knowledge and Auxiliary Tasks (2020.acl-main)

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Challenge: Hierarchical text classification is a complex task that requires extended training time and a large number of parameters.
Approach: They propose a top-up-classification task using dictionaries and auxiliary task from external dictionary definitions.
Outcome: The proposed method outperforms previous studies using a reduced number of parameters in two well-known English datasets.
Back-Translation as Strategy to Tackle the Lack of Corpus in Natural Language Generation from Semantic Representations (D19-63)

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Challenge: Abstract Meaning Representation and Brazilian Portuguese (BP) are selected as semantic representation and language, respectively.
Approach: They propose to use Brazilian Portuguese and Abstract Meaning Representation as semantic representations for NLG.
Outcome: The proposed methods were evaluated on two datasets (one automatically generated and another human-generated) to compare the performance in a real context.

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