Challenge: Current chart-based Question Answering approaches address structural, visual or simple data retrieval-type questions with fixed-vocabulary answers.
Approach: They employ a neural semantic parser to transform NL questions into SQL programs . they use a probabilistic context-free grammar to generate NL queries from a schema .
Outcome: The proposed approach achieves State-of-the-Art (SOTA) results on reasoning-based queries.

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Multilingual Generation and Answering of Questions from Texts and Knowledge Graphs (2023.findings-emnlp)

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Challenge: Existing methods for QG-QA are limited to English, but can be used in other languages.
Approach: They propose to bring multilinguality to multimodal QG-QA by using Brazilian Portuguese and Russian data.
Outcome: The proposed approach outperforms a baseline on English and can handle both languages.
ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch (2026.acl-long)

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Challenge: Existing open-source vision language models lack high-quality training data for chart reasoning . current models are simplistic and repetitive, while associated QA pairs are prone to hallucinations .
Approach: They propose a framework to synthesize complex charts and reliable reasoning data from scratch.
Outcome: Experimental results show that ChartVerse-8B surpasses existing models in QA and difficulty . lack of high-quality training data hampers development of open-source models .
Chart Question Answering from Real-World Analytical Narratives (2025.acl-srw)

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Challenge: a dataset for chart question answering is constructed from visualization notebooks . data visualizations are an essential modality for communicating complex information about data.
Approach: They propose a dataset for chart question answering constructed from visualization notebooks . they use real-world, multi-view charts paired with natural language questions .
Outcome: The proposed dataset is constructed from student-authored visualization notebooks . it features real-world, multi-view charts paired with natural language questions . initial evaluations highlight significant performance gaps .
Complex Reasoning in Natural Language (2023.acl-tutorials)

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Challenge: Recent research shows that pretrained language models are often brittle for complex reasoning tasks.
Approach: They propose to use pre-trained language models to teach machines to reason over texts . they will review recent promising approaches to tackling complex reasoning tasks .
Outcome: This tutorial reviews promising approaches to complex reasoning tasks . it reviews the methods that can be used to augment models with robustness .
Improving Unsupervised Question Answering via Summarization-Informed Question Generation (2021.emnlp-main)

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Challenge: Question Generation (QG) is the production of meaningful questions given a set of input passages and corresponding answers.
Approach: They propose a method which uses questions generated heuristically from news summaries as a source of training data for a QG system.
Outcome: The proposed method outperforms previous unsupervised models on three in-domain datasets and three out-of-domain ones.
Fusing Context Into Knowledge Graph for Commonsense Question Answering (2021.findings-acl)

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Challenge: Existing methods to combine language modeling and knowledge graphs (KG) lack the context to provide a more precise understanding of the concepts.
Approach: They propose to use external entity descriptions to provide contextual information for commonsense question answering models.
Outcome: The proposed model achieves state-of-the-art among non-generative models in OpenBookQA and is the first of its kind in the field.
SynDARin: Synthesising Datasets for Automated Reasoning in Low-Resource Languages (2025.coling-main)

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Challenge: Question Answering datasets are scarce for languages other than English due to the cost and difficulties of collection and manual annotation.
Approach: They propose a method for generating and validating QA datasets for low-resource languages . they use English data as context to generate synthetic multiple-choice (MC) question-answer pairs .
Outcome: The proposed method maintains quality, reduces likelihood of factual errors, and circumvents costly annotation.
ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning (2022.findings-acl)

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Challenge: Existing datasets that focus on complex reasoning questions do not address such questions as they are template-based and answers come from a fixed-vocabulary.
Approach: They propose a large-scale benchmark that uses visual and logical reasoning to answer questions using a transformer-based model.
Outcome: The proposed models achieve state-of-the-art on the previous datasets and on the current one, but also show that they have several challenges in answering complex reasoning questions.
Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities (2025.emnlp-main)

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Challenge: Large language models (LLMs) have shown remarkable performance on question-answering tasks due to their superior capabilities in natural language understanding and generation.
Approach: They propose a structured taxonomy that categorizes the methodology of synthesizing LLMs and knowledge graphs for QA according to the categories of QA and the KG’s role when integrating with LLM.
Outcome: The proposed taxonomy categorizes the methods according to the categories of QA and the KG’s role when integrating with LLMs.
STL-CQA: Structure-based Transformers with Localization and Encoding for Chart Question Answering (2020.emnlp-main)

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Challenge: Chart Question Answering (CQA) is a task of answering natural language questions about visualisations in the chart image.
Approach: They propose a method for Chart Question Answering which improves the question/answering through sequential elements localization, question encoding and then, a structural transformer-based learning approach.
Outcome: The proposed method outperforms state-of-the-art methods on various chart Q/A datasets while outperforming even human baseline.

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