Question answering requiring discrete reasoning, e.g., arithmetic computing, comparison, and counting, over knowledge is a challenging task.
In this paper, we propose UniRPG, a semantic-parsing-based approach advanced in interpretability and scalability, to perform Unified discrete Reasoning over heterogeneous knowledge resources, i.e., table and text, as Program Generation. 
Concretely, UniRPG consists of a neural programmer and a symbolic program executor,
where a program is the composition of a set of pre-defined general atomic and higher-order operations and arguments extracted from table and text.
First, the programmer parses a question into a program by generating operations and copying arguments, and then, the executor derives answers from table and text based on the program.
To alleviate the costly program annotation issue, we design a distant supervision approach for programmer learning, where pseudo programs are automatically constructed without annotated derivations.
Extensive experiments on the TAT-QA dataset show that UniRPG achieves tremendous improvements and enhances interpretability and scalability compared with previous state-of-the-art methods, even without derivation annotation.
Moreover, it achieves promising performance on the textual dataset DROP without derivation annotation.

UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation

Machine reading comprehension (MRC) that requires discrete reasoning involving symbolic operations, e.g., addition, sorting, and counting, is a challenging task. According to this nature, semantic parsing-based methods predict interpretable but complex logical forms. However, logical form generation is nontrivial and even a little perturbation in a logical form will lead to wrong answers. To alleviate this issue, multi-predictor -based methods are proposed to directly predict different types of answers and achieve improvements. However, they ignore the utilization of symbolic operations and encounter a lack of reasoning ability and interpretability. To inherit the advantages of these two types of methods, we propose OPERA, an operation-pivoted discrete reasoning framework, where lightweight symbolic operations (compared with logical forms) as neural modules are utilized to facilitate the reasoning ability and interpretability. Specifically, operations are first selected and then softly executed to simulate the answer reasoning procedure. Extensive experiments on both DROP and RACENum datasets show the reasoning ability of OPERA. Moreover, further analysis verifies its interpretability.

OPERA: Operation-Pivoted Discrete Reasoning over Text

**Topics:** Theme Track 
Natural Language Generation 
Question Answering 
Resources and Evaluation

Virtual Poster Session 16

poster

## Welcome to EMNLP 2022!
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## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
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NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Yongwei Zhou

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UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation

OPERA: Operation-Pivoted Discrete Reasoning over Text

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