Papers by Yasemin Altun
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. |
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. |
Multimodal Chart Retrieval: A Comparison of Text, Table and Image Based Approaches (2024.naacl-long)
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| Challenge: | Multimodal chart retrieval is a task of retrieving information from a multimodal dataset, given a query. |
| Approach: | They compare four different approaches to multimodal chart retrieval . they use chart derendering, chart derending, table retrieval and Tab-GTR . |
| Outcome: | The proposed model outperforms the previous methods on the NQ-Tables benchmark and is significantly more efficient on in-distribution data. |
Answering Conversational Questions on Structured Data without Logical Forms (D19-1)
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| Challenge: | Existing approaches to answering sequential questions based on structured objects do not use a logical form as an intermediate representation. |
| Approach: | They propose a novel approach to answering sequential questions based on structured objects without using a logical form as an intermediate representation. |
| Outcome: | The proposed approach is competitively tested on the Sequential Question Answering (SQA) task. |
Generating Logical Forms from Graph Representations of Text and Entities (P19-1)
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| Challenge: | Recent approaches to semantic parsing have cast it as a sequence-to-sequence task, with strong results. |
| Approach: | They propose a Graph Neural Network architecture to incorporate information about relevant entities and their relations during parsing. |
| Outcome: | The proposed approach outperforms the state-of-the-art in several tasks without pre-training and outperformed existing approaches when combined with BERT pre-trainment. |
Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data (2021.findings-emnlp)
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| Challenge: | Existing methods to improve semantic parsing performance on target languages are limited. |
| Approach: | They propose a Translate-and-Fill method that produces silver training data for a multilingual semantic parser. |
| Outcome: | The proposed method produces silver training data for a multilingual parser. |