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Found 9 papers, 2 papers with code
DePlot: One-shot visual language reasoning by plot-to-table translation
Compared with a SOTA model finetuned on more than >28k data points, DePlot+LLM with just one-shot prompting achieves a 24. 0% improvement over finetuned SOTA on human-written queries from the task of chart QA.
Chart Question Answering
Factual Inconsistency Detection in Chart Captioning
+3
MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering
Visual language data such as plots, charts, and infographics are ubiquitous in the human world.
Ranked #1 on
Visual Question Answering
on PlotQA-D2
Table-To-Text generation and pre-training with TabT5
Encoder-only transformer models have been successfully applied to different table understanding tasks, as in TAPAS (Herzig et al., 2020).
LAD: Language Models as Data for Zero-Shot Dialog
To facilitate zero-shot generalization in taskoriented dialog, this paper proposes Language Models as Data (LAD).
What Did You Say? Task-Oriented Dialog Datasets Are Not Conversational!?
High-quality datasets for task-oriented dialog are crucial for the development of virtual assistants.
Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data
While multilingual pretrained language models (LMs) fine-tuned on a single language have shown substantial cross-lingual task transfer capabilities, there is still a wide performance gap in semantic parsing tasks when target language supervision is available.
Answering Conversational Questions on Structured Data without Logical Forms
We present a novel approach to answering sequential questions based on structured objects such as knowledge bases or tables without using a logical form as an intermediate representation.
Generating Logical Forms from Graph Representations of Text and Entities
Structured information about entities is critical for many semantic parsing tasks.
