Papers by Lina M. Rojas Barahona
Investigating the Effect of Relative Positional Embeddings on AMR-to-Text Generation with Structural Adapters (2023.eacl-main)
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| Challenge: | Recent approaches to text generation from Abstract Meaning Representation (AMR) have been based on neural-centered encoderdecoder architectures. |
| Approach: | They propose a structure-aware adapter which injects the input graph connectivity within PLMs using Graph Neural Networks. |
| Outcome: | The proposed adapter is robust to a variety of approaches and can be used to generate Graph-to-Text representations. |
KGConv, a Conversational Corpus Grounded in Wikidata (2024.lrec-main)
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| Challenge: | a large corpus of 71k English conversations contains on average 8.6 questions . Unlike open domain and task-oriented dialogues, information seeking conversations are driven by the desire to acquire or evaluate knowledge. |
| Approach: | They propose a large corpus of 71k English conversations where each question-answer pair is grounded in a Wikidata fact. |
| Outcome: | The proposed dataset can be used for knowledge-based, conversational question generation . it can also be used to generate single-turn questions from Wikidata triples, question rewriting, question answering from conversation or knowledge graphs and quiz generation. |
SPARQL-to-Text Question Generation for Knowledge-Based Conversational Applications (2022.aacl-main)
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| Challenge: | a paper focuses on the generation of natural language questions based on SPARQL queries . knowledge-based approaches have become popular in the field of question answering and dialogue . |
| Approach: | This paper focuses on the generation of natural language questions based on SPARQL queries . it uses 4 knowledge-based QA corpora homogenized for the task and a new challenge set is introduced . |
| Outcome: | The proposed task is based on the generation of questions in a conversational context. |
Few-Shot Structured Policy Learning for Multi-Domain and Multi-Task Dialogues (2023.findings-eacl)
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| Challenge: | Reinforcement learning is widely adopted to model dialogue managers in task-oriented dialogues, but the user simulator provided by state-of-the-art dialogue frameworks are only rough approximations of human behaviour. |
| Approach: | They propose to use structured policies to improve sample efficiency when learning on multi-domain and multi-task environments. |
| Outcome: | The proposed policies improve sample efficiency and performance on multi-domain and multi-task environments. |
CoQAR: Question Rewriting on CoQA (2022.lrec-1)
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| Challenge: | Existing systems that ask questions in a conversational context may have contextual dependencies that make the understanding difficult. |
| Approach: | They propose to rewrite questions into an out-of-context form to facilitate understanding . they propose to use this form to train and evaluate conversational question answering models . |
| Outcome: | The proposed model can be used in the supervised learning of three tasks: question paraphrasing, question rewriting and conversational question answering. |
Hyperbolic Temporal Knowledge Graph Embeddings with Relational and Time Curvatures (2021.findings-acl)
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| Challenge: | Existing knowledge Graph models for Link Prediction are insensitive to time. |
| Approach: | They propose a time-aware extension of ATTH model which defines curvature of a Riemannian manifold as the product of both relation and time. |
| Outcome: | The proposed model can achieve competitive or even better performance than the state-of-the-art model on Temporal KGs, albeit its nontemporality. |