Papers by Syrine Krichene
Open Domain Question Answering over Tables via Dense Retrieval (2021.naacl-main)
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| Challenge: | Recent advances in open-domain QA focus on retrieving textual passages . a retriever designed to handle tabular context can improve retrieval quality . |
| Approach: | They propose a tabular-based retrieval model that improves retrieval quality over a BERT-based retriever. |
| Outcome: | The proposed retriever improves retrieval quality with mined hard negatives over a BERT-based retriever. |
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering (2023.acl-long)
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Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, Julian Eisenschlos
| Challenge: | Visual language models that are pretraining on natural images or image-text pairs crawled from the web perform poorly on visual language tasks such as ChartQA and ChartQA. |
| Approach: | They propose to perform several pretraining tasks that cover plot deconstruction and numerical reasoning which are key capabilities in visual language modeling. |
| Outcome: | The proposed model outperforms state-of-the-art methods on benchmarks such as PlotQA and ChartQA by as much as 20%. |
Table-To-Text generation and pre-training with TabT5 (2022.findings-emnlp)
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| Challenge: | Large language models (LLMs) are limited when it comes to structured or semi-structured domains like tables. |
| Approach: | They propose an encoder-decoder model that generates natural language text based on tables and textual inputs. |
| Outcome: | TabT5 achieves 15% increase in sequence accuracy on spreadsheet formula prediction and data-to-text generation domains. |
Understanding tables with intermediate pre-training (2020.findings-emnlp)
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| Challenge: | Textual entailment is well studied, but is less well studied for table enlargement . a new dataset of millions of examples is used to train the model . |
| Approach: | They adapt a table-based BERT model to recognize entailment from a dataset . they evaluate table pruning techniques as a pre-processing step to improve model efficiency . |
| Outcome: | The proposed model improves training and prediction efficiency at a moderate drop in accuracy. |
DePlot: One-shot visual language reasoning by plot-to-table translation (2023.findings-acl)
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Fangyu Liu, Julian Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun
| Challenge: | Existing models for visual language reasoning require tens of thousands of training examples and their reasoning capabilities are limited. |
| Approach: | They propose a one-shot solution to visual language reasoning by combining plot-to-text translation and reasoning over the translated text into a modality conversion module. |
| Outcome: | The proposed method improves on human-written queries on plots and charts compared with a fine-tuned SOTA model on human queries. |
DoT: An efficient Double Transformer for NLP tasks with tables (2021.findings-acl)
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| Challenge: | Recent studies have shown that transformer-based approaches to NLP tasks are slow and require computational and memory costs. |
| Approach: | They propose a transformer-based model that decomposes a problem into two sub-tasks and a pruning transformer that takes as input the pruning scores. |
| Outcome: | The proposed model improves training and inference time by at least 50% for a small drop in accuracy and also enables the model to maintain similar accuracy as slower baseline models. |
Faithful Chart Summarization with ChaTS-Pi (2024.acl-long)
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| Challenge: | Chart-to-summary generation can help explore data, communicate insights, and help the visually impaired. |
| Approach: | They propose a reference-free chart summarization metric for scoring faithfulness . they use human raters to fix and rank candidate summaries from any chart-summarization model . |
| Outcome: | The proposed metric scores the summarization faithfulness according to human ratings better than reference-based metrics, either learned or n-gram based. |