OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering (2022.naacl-main)
Copied to clipboard
| Challenge: | a table-based question answering system requires complex reasoning and alignment between questions and tables. |
| Approach: | They propose a table-based QA model that consumes both natural and synthetic data . they combine retrieval with masking to pair natural sentences with QA . |
| Outcome: | The proposed model outperforms existing models in few-shot and full settings and on WikiTableQuestions. |
Similar Papers
Multitask Pretraining with Structured Knowledge for Text-to-SQL Generation (2023.acl-long)
Copied to clipboard
Robert Giaquinto, Dejiao Zhang, Benjamin Kleiner, Yang Li, Ming Tan, Parminder Bhatia, Ramesh Nallapati, Xiaofei Ma
| Challenge: | Existing methods for learning representations of structured knowledge are limited to the minority of people with technical skills. |
| Approach: | They propose a large pretraining dataset and strategy for learning representations of text, tables, and SQL code that leverages the entire context of the problem. |
| Outcome: | The proposed model improves on two SQL tasks and shows a 1.7 and 2.2 percentage point improvement over existing methods. |
Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering (2022.coling-1)
Copied to clipboard
| Challenge: | Generative question answering (QA) models generate answers to complex questions, but their mechanism for doing so is still poorly understood. |
| Approach: | They decompose multi-hop questions into multiple corresponding single-hop question chains and find marked inconsistency in QA models’ answers on these pairs of ostensibly identical question chains. |
| Outcome: | The proposed models lack zero-shot multi-hop reasoning ability when trained on single-hop questions and on logical forms. |
Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Among recent NLP research, multi-document processing is gaining increasing attention due to the need to handle and process an increasing amount of textual data and available documents online. |
| Approach: | They propose to pre-train a generic multi-document model from a cross-document question answering pre-training objective by generating salient sentences from one document and challenging it to recover the sentence from which it was generated. |
| Outcome: | The proposed model outperforms zero-shot GPT-3.5 and GPT-4 in multiple document tasks and generates the correct answer and the salient sentence from a salient document. |
Question Answering Infused Pre-training of General-Purpose Contextualized Representations (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing pretraining objectives for question answering (QA) are not optimized for being immediately useful without fine-tuning. |
| Approach: | They propose a pre-training objective based on question answering (QA) that is based more directly on context. |
| Outcome: | The proposed model matches predictions of a more accurate cross-encoder model on 80 million synthesized QA pairs and achieves large improvements over previous state-of-the-art models on paraphrase detection and fewshot named entity recognition. |
A Pipeline for Generating, Annotating and Employing Synthetic Data for Real World Question Answering (2022.emnlp-demos)
Copied to clipboard
| Challenge: | Question Answering (QA) is a growing area of research . state-of-the-art QA models struggle on out-of domain documents without fine-tuning . |
| Approach: | They propose a pipeline for validating and training QA data and an interface for human annotation. |
| Outcome: | The proposed pipeline improves QA performance on domain-specific datasets while preserving the accuracy of the model. |
FewshotQA: A simple framework for few-shot learning of question answering tasks using pre-trained text-to-text models (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing pre-trained models need fine-tuning on tens of thousands of examples to achieve good results. |
| Approach: | They propose a framework that leverages pre-trained text-to-text models and aligns them with their pre-training framework. |
| Outcome: | The proposed framework outperforms the XLM-Roberta-large on multiple QA benchmarks and is applicable to multilingual situations. |
OpenRT: An Open-source Framework for Reasoning Over Tabular Data (2023.acl-demo)
Copied to clipboard
| Challenge: | Existing table pre-training methods are benchmarked on a limited number of datasets with varying configurations, resulting in a lack of unified, standardized, fair, and comprehensive comparison between methods. |
| Approach: | They propose to use OpenRT to reproduce existing table pre-training models and develop new models quickly. |
| Outcome: | The proposed framework reproduces existing table pre-training models and compares them against four question answering, one fact checking, and one faithful text generation datasets. |
TaPas: Weakly Supervised Table Parsing via Pre-training (2020.acl-main)
Copied to clipboard
| Challenge: | Answering natural language questions over tables is often seen as a semantic parsing task. |
| Approach: | They propose an approach to question answering over tables without generating logical forms by selecting table cells and optionally applying a corresponding aggregation operator. |
| Outcome: | The proposed approach outperforms or rivals existing models on three different datasets and performs on par with the state-of-the-art on WikiSQL and WikiTQ. |
Learning Relational Decomposition of Queries for Question Answering from Tables (2024.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to Table Question-Answering focus on generating answers directly from inputs, but there are limitations when executing numerical operations. |
| Approach: | They propose to imitate a restricted subset of SQL-like algebraic operations and use them to generate a query. |
| Outcome: | The proposed methods bridge the gap between semantic parsing and direct answering methods and offer valuable insights into which types of operations should be predicted by a generative architecture and which should be executed by an external algorithm. |
STable: Table Generation Framework for Encoder-Decoder Models (2024.eacl-long)
Copied to clipboard
Michał Pietruszka, Michał Turski, Łukasz Borchmann, Tomasz Dwojak, Gabriela Nowakowska, Karolina Szyndler, Dawid Jurkiewicz, Łukasz Garncarek
| Challenge: | Existing approaches to infer text-to-table neural models are limited to raw text, but the proposed framework is capable of unifying a variety of problems involving natural language. |
| Approach: | They propose a framework for text-to-table neural models that utilizes a generalized sequential method that comprehends information from all cells in the table. |
| Outcome: | The proposed framework outperforms previous approaches on several challenging datasets and outperformed existing models by up to 15%. |