Papers by Ramón Fernandez Astudillo
X-FACTOR: A Cross-metric Evaluation of Factual Correctness in Abstractive Summarization (2022.emnlp-main)
Copied to clipboard
Subhajit Chaudhury, Sarathkrishna Swaminathan, Chulaka Gunasekara, Maxwell Crouse, Srinivas Ravishankar, Daiki Kimura, Keerthiram Murugesan, Ramón Fernandez Astudillo, Tahira Naseem, Pavan Kapanipathi, Alexander Gray
| Challenge: | Abstractive summarization models produce factually inconsistent summaries that are not supported by the original article. |
| Approach: | They propose a fact-aware filtering mechanism that improves the factuality of abstractive summarization models. |
| Outcome: | The proposed method improves the quality of training data and the factuality of generated summaries. |
On the Importance of Diversity in Question Generation for QA (2020.acl-main)
Copied to clipboard
| Challenge: | In this paper, we show that textual diversity in automatic question generation is beneficial for downstream QA. |
| Approach: | They propose to use textual diversity to promote automatic question generation as a quality measure for QA. |
| Outcome: | The proposed measure of QG quality correlates well with evaluation on QA. |
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering (2021.findings-acl)
Copied to clipboard
Pavan Kapanipathi, Ibrahim Abdelaziz, Srinivas Ravishankar, Salim Roukos, Alexander Gray, Ramón Fernandez Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio Gliozzo, Sairam Gurajada, Hima Karanam, Naweed Khan, Dinesh Khandelwal, Young-Suk Lee, Yunyao Li, Francois Luus, Ndivhuwo Makondo, Nandana Mihindukulasooriya, Tahira Naseem, Sumit Neelam, Lucian Popa, Revanth Gangi Reddy, Ryan Riegel, Gaetano Rossiello, Udit Sharma, G P Shrivatsa Bhargav, Mo Yu
| Challenge: | Existing approaches face challenges including complex question understanding and lack of large end-to-end training datasets. |
| Approach: | They propose a modular knowledge base question answering system that leverages AMR parses for task-independent question understanding. |
| Outcome: | The proposed system achieves state-of-the-art performance on two prominent KBQA datasets based on DBpedia. |
Bootstrapping Multilingual AMR with Contextual Word Alignments (2021.eacl-main)
Copied to clipboard
Janaki Sheth, Young-Suk Lee, Ramón Fernandez Astudillo, Tahira Naseem, Radu Florian, Salim Roukos, Todd Ward
| Challenge: | Abstract Meaning Representation (AMR) is a sentence-level graph that is biased towards English. |
| Approach: | They propose a technique for foreign-text-to-English AMR alignment using contextual word alignment between English and foreign language tokens. |
| Outcome: | The proposed technique outperforms the best results for German, Italian, Spanish and Chinese. |
Pushing the Limits of AMR Parsing with Self-Learning (2020.findings-emnlp)
Copied to clipboard
Young-Suk Lee, Ramón Fernandez Astudillo, Tahira Naseem, Revanth Gangi Reddy, Radu Florian, Salim Roukos
| Challenge: | Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years due to the impact of transfer learning and the development of novel architectures specific to AMR. |
| Approach: | They propose to use AMR annotations to generate synthetic text and refine actions oracle without additional human annotations for AMR parsing. |
| Outcome: | The proposed models improve on AMR 1.0 and 2.0 without human annotations. |
AMR Parsing with Action-Pointer Transformer (2021.naacl-main)
Copied to clipboard
| Challenge: | Abstract Meaning Representation parsing is a sentence-to-graph prediction task . graph nodes are semantically based on one or more sentence tokens, so implicit alignments can be derived. |
| Approach: | They propose a transition-based system that decouples hard-attention over sentences with a target-side action pointer mechanism to decouple source tokens from node representations and address alignments. |
| Outcome: | The proposed system achieves the second best Smatch score on AMR 2.0 (81.8) it decouples source tokens from node representations and addresses alignments, but lacks expressiveness. |
Transition-based Parsing with Stack-Transformers (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing parsing systems use local or global models of the parser state to improve performance. |
| Approach: | They propose to modify the sequence-to-sequence Transformer to model global or local parser states in transition-based parsing. |
| Outcome: | The proposed model significantly improves performance on dependency and Abstract Meaning Representation (AMR) parsing tasks. |
Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent work shows that pre-trained sequence-to-sequence Transformer models are effective in predicting linearized Abstract Meaning Representation graphs. |
| Approach: | They propose a structure-aware transition-based approach to AMR parsing that integrates general pre-trained sequence-to-sequence language models with a structured transition set. |
| Outcome: | The proposed approach retains the desirable properties of previous approaches while reaching the new parsing state of the art for AMR 2.0. |
Structural Guidance for Transformer Language Models (2021.acl-long)
Copied to clipboard
| Challenge: | Pre-trained Transformer language models have proven remarkably successful in learning generic transferable linguistic representations without resorting to data intensive pre-training. |
| Approach: | They propose to combine a generative parsing and a structural scaffolding idea to guide the model's representation via additional structure loss that separates the incremental constituency parse. |
| Outcome: | The proposed models achieve impressive perplexity results on language modelling datasets, perform well on grammatical judgments, and provide useful linguistic representations that benefit a wide range of downstream tasks. |
GPT-too: A Language-Model-First Approach for AMR-to-Text Generation (2020.acl-main)
Copied to clipboard
Manuel Mager, Ramón Fernandez Astudillo, Tahira Naseem, Md Arafat Sultan, Young-Suk Lee, Radu Florian, Salim Roukos
| Challenge: | Existing approaches to generating text from AMRs focus on training sequence-to-sequence or graph-tosequent models on annotated data. |
| Approach: | They propose a strong pre-trained language model with cycle consistency-based re-scoring to generate AMR text. |
| Outcome: | The proposed model outperforms existing methods on the English LDC2017T10 dataset. |