Papers by Laura Perez-Beltrachini
Fine-Grained Natural Language Inference Based Faithfulness Evaluation for Diverse Summarisation Tasks (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing approaches to evaluate summary faithfulness are sub-optimal due to the granularity level considered for premises and hypotheses. |
| Approach: | They propose a novel approach that uses a variable premise size and simplifies summary sentences into shorter hypotheses. |
| Outcome: | The proposed model performs better on diverse summarisation tasks than existing models. |
Generating Summaries with Topic Templates and Structured Convolutional Decoders (P19-1)
Copied to clipboard
| Challenge: | Existing neural generation approaches create multi-sentence text as a single sequence . Existing approaches create multiple sentences as if they were a sequence based on content structure . |
| Approach: | They propose a structured convolutional decoder that is guided by the content structure of target summaries. |
| Outcome: | The proposed model outperforms existing decoders on three datasets representing different domains. |
Improving User Controlled Table-To-Text Generation Robustness (2023.findings-eacl)
Copied to clipboard
| Challenge: | In experiments, models perform well on test sets coming from the same distribution as the train data but their performance drops when evaluated on realistic noisy user inputs. |
| Approach: | They propose a user controlled table-to-text generation task where users explore the content in a table by selecting cells and reading a natural language description thereof. |
| Outcome: | The proposed model gains 4.85 BLEU points on user noisy test cases and 1.4 on clean test cases. |
Bootstrapping Generators from Noisy Data (N18-1)
Copied to clipboard
| Challenge: | Existing methods for data-to-text generation focus on learning correspondences between structured data and associated texts. |
| Approach: | They aim to bootstrap generators from large scale datasets where data and related texts are loosely aligned. |
| Outcome: | The proposed model improves on a vanilla encoder-decoder which relies on soft attention. |
Models and Datasets for Cross-Lingual Summarisation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent years have witnessed increased interest in abstractive summarisation thanks to the popularity of neural network models and the availability of datasets containing hundreds of thousands of document-summary pairs. |
| Approach: | They propose to create a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in . target language. |
| Outcome: | The proposed task can be applied to several other languages and covers twelve languages and directions. |
Semantic Parsing for Conversational Question Answering over Knowledge Graphs (2023.eacl-main)
Copied to clipboard
| Challenge: | Recent years have seen an increasing number of applications aiming to build conversational interfaces based on information retrieval and user recommendation. |
| Approach: | They develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof. |
| Outcome: | The proposed parsers can be used to ground questions into queries over definitions in a knowledge graph with large vocabularies. |
Leveraging Entailment Judgements in Cross-Lingual Summarisation (2024.findings-acl)
Copied to clipboard
| Challenge: | Synthetically created cross-lingual summarisation datasets are prone to include document-summary pairs where the reference summary is unfaithful to the corresponding document. |
| Approach: | They propose to use off-the-shelf cross-lingual Natural Language Inference to evaluate faithfulness of reference and model generated summaries and use unlikelihood loss to teach a model about unfaithful summary sequences. |
| Outcome: | The proposed approach evaluates faithfulness of reference and model generated summaries and uses unlikelihood loss to teach a model about unfaithful summary sequences. |