Papers by Pedro Szekely
Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering (2020.findings-emnlp)
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| Challenge: | Existing QA systems do not have commonsense knowledge or cannot reason with it. |
| Approach: | They propose to augment a general commonsense QA framework with a knowledgeable path generator by extrapolating existing paths from a KG with 'state-of-the-art' language model. |
| Outcome: | The generated paths are interpretable, novel, and relevant to the task. |
Efficient and Accurate Contextual Re-Ranking for Knowledge Graph Question Answering (2024.lrec-main)
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Kexuan Sun, Nicolaas Paul Jedema, Karishma Sharma, Ruben Janssen, Jay Pujara, Pedro Szekely, Alessandro Moschitti
| Challenge: | Existing approaches to QA over textual data are based on a "retrieve-then-generate" pipeline. |
| Approach: | They propose a "triple-level" labeling strategy that infers fine-grained labels and trains a re-ranker to improve relevance of retrieved triples. |
| Outcome: | The proposed pipeline improves on prior KGQA systems by 5.56% Exact Match. |
Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning (2022.naacl-main)
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| Challenge: | Controlled table-to-text generation is a new approach to generate textual descriptions for highlighted subparts of a table. |
| Approach: | They propose an equivariance learning framework which encodes tables with a structure-aware self-attention mechanism and a positional encoding mechanism to preserve relative position of tokens in the same cell. |
| Outcome: | The proposed framework is free to be plugged into existing table-to-text generation models and has improved T5-based models to offer better performance on ToTTo and HiTab. |
Table-based Fact Verification With Salience-aware Learning (2021.findings-emnlp)
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| Challenge: | Existing methods for fact verification use tabular data with tokens, but training requires labeled training data. |
| Approach: | They propose a system that identifies token-level salience in the statement with probing-based saliency estimation. |
| Outcome: | The proposed system improves on TabFact benchmark by replacing non-salient terms with tokens. |
Representing Numbers in NLP: a Survey and a Vision (2021.naacl-main)
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| Challenge: | Numeracy is an essential skill for language understanding since numbers are often interspersed in text. |
| Approach: | They propose a comprehensive taxonomy of tasks and methods to represent numbers in text . they synthesize best practices for representing numbers in texts and articulate a vision for holistic numeracy . |
| Outcome: | The proposed model synthesizes best practices for representing numbers in text . it argues that the model is more effective than other approaches . |
Speechworthy Instruction-tuned Language Models (2024.emnlp-main)
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Hyundong Cho, Nicolaas Jedema, Leonardo Ribeiro, Karishma Sharma, Pedro Szekely, Alessandro Moschitti, Ruben Janssen, Jonathan May
| Challenge: | Current instruction tuned language models are trained on textual preference data and therefore not aligned to speech domain. |
| Approach: | They propose to use radio-industry best practices to prompt and learn speech-based preference data to improve speech-suitability of popular instruction tuned language models. |
| Outcome: | The proposed methods achieve the best win rates in head-to-head comparisons, resulting in preferred or tied to the base model in 76.2% of comparisons on average. |
PaCo: Preconditions Attributed to Commonsense Knowledge (2022.findings-emnlp)
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| Challenge: | Existing language models can reason with circumstantial preconditions of commonsense knowledge, but they do not understand the circumstancial precondition. |
| Approach: | They propose to use a dataset to examine the ability of existing language models to understand circumstantial preconditions to improve their reasoning with commonsense knowledge. |
| Outcome: | The proposed task shows that human reasoning with preconditions is an open challenge. |