# Program Transfer for Answering Complex Questions over Knowledge Bases

arXiv: https://arxiv.org/abs/2110.05743 · HTML: https://mlsanity.com/paper/2110.05743/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 49 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [The Web as a Knowledge-base for Answering Complex Questions](https://mlsanity.com/paper/1803.06643/) (2018)
- [Key-Value Memory Networks for Directly Reading Documents](https://mlsanity.com/paper/1606.03126/) (2016)
- [PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text](https://mlsanity.com/paper/1904.09537/) (2019)
- [Variational Reasoning for Question Answering with Knowledge Graph](https://mlsanity.com/paper/1709.04071/) (2017)
- [Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text](https://mlsanity.com/paper/1809.00782/) (2018)
- [Language to Logical Form with Neural Attention](https://mlsanity.com/paper/1601.01280/) (2016)
- [Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision](https://mlsanity.com/paper/1611.00020/) (2016)
- [Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals](https://mlsanity.com/paper/2101.03737/) (2021)
- [A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions](https://mlsanity.com/paper/2105.11644/) (2021)
- [Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing](https://mlsanity.com/paper/2010.03546/) (2020)
- [TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph](https://mlsanity.com/paper/2104.07302/) (2021)
- [Question Answering with Subgraph Embeddings](https://mlsanity.com/paper/1406.3676/) (2014)
- [Linguistic generalization and compositionality in modern artificial neural networks](https://mlsanity.com/paper/1904.00157/) (2019)
- [Leveraging Language to Learn Program Abstractions and Search Heuristics](https://mlsanity.com/paper/2106.11053/) (2021)
- [Question Answering on Freebase via Relation Extraction and Textual Evidence](https://mlsanity.com/paper/1603.00957/) (2016)
- [Lambda Dependency-Based Compositional Semantics](https://mlsanity.com/paper/1309.4408/) (2013)
- [Cross-domain Semantic Parsing via Paraphrasing](https://mlsanity.com/paper/1704.05974/) (2017)
- [Learning Structured Natural Language Representations for Semantic Parsing](https://mlsanity.com/paper/1704.08387/) (2017)
- [Learning a Natural Language Interface with Neural Programmer](https://mlsanity.com/paper/1611.08945/) (2016)
- [Neural Semantic Parsing over Multiple Knowledge-bases](https://mlsanity.com/paper/1702.01569/) (2017)
- [Transfer Learning for Neural Semantic Parsing](https://mlsanity.com/paper/1706.04326/) (2017)
- [Zero-Shot Semantic Parsing for Instructions](https://mlsanity.com/paper/1911.08827/) (2019)

## Built on by (most cited first, 30 of 34)

- [Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph](https://mlsanity.com/paper/2307.07697/) (2023)
- [DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases](https://mlsanity.com/paper/2210.00063/) (2022)
- [TIARA: Multi-grained Retrieval for Robust Question Answering over Large Knowledge Bases](https://mlsanity.com/paper/2210.12925/) (2022)
- [Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments](https://mlsanity.com/paper/2212.09736/) (2022)
- [KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases](https://mlsanity.com/paper/2308.11761/) (2023)
- [FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering](https://mlsanity.com/paper/2308.12060/) (2023)
- [ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models](https://mlsanity.com/paper/2310.08975/) (2023)
- [SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions](https://mlsanity.com/paper/2407.11417/) (2024)
- [FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base Question Answering](https://mlsanity.com/paper/2306.14722/) (2023)
- [Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling](https://mlsanity.com/paper/2305.11719/) (2023)
- [GraphQ IR: Unifying the Semantic Parsing of Graph Query Languages with One Intermediate Representation](https://mlsanity.com/paper/2205.12078/) (2022)
- [SUQL: Conversational Search over Structured and Unstructured Data with Large Language Models](https://mlsanity.com/paper/2311.09818/) (2023)
- [Reasoning over Hierarchical Question Decomposition Tree for Explainable Question Answering](https://mlsanity.com/paper/2305.15056/) (2023)
- [Knowledge Crosswords: Geometric Knowledge Reasoning with Large Language Models](https://mlsanity.com/paper/2310.01290/) (2023)
- [FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs](https://mlsanity.com/paper/2501.09957/) (2025)
- [Beyond Seen Data: Improving KBQA Generalization Through Schema-Guided Logical Form Generation](https://mlsanity.com/paper/2502.12737/) (2025)
- [Do I have the Knowledge to Answer? Investigating Answerability of Knowledge Base Questions](https://mlsanity.com/paper/2212.10189/) (2022)
- [Pay More Attention to Relation Exploration for Knowledge Base Question Answering](https://mlsanity.com/paper/2305.02118/) (2023)
- [Few-shot Transfer Learning for Knowledge Base Question Answering: Fusing Supervised Models with In-Context Learning](https://mlsanity.com/paper/2311.08894/) (2023)
- [How Proficient Are Large Language Models in Formal Languages? An In-Depth Insight for Knowledge Base Question Answering](https://mlsanity.com/paper/2401.05777/) (2024)
- [RJE: A Retrieval-Judgment-Exploration Framework for Efficient Knowledge Graph Question Answering with LLMs](https://mlsanity.com/paper/2510.01257/) (2025)
- [Knowledge Base Question Answering for Space Debris Queries](https://mlsanity.com/paper/2305.19734/) (2023)
- [Data Distribution Bottlenecks in Grounding Language Models to Knowledge Bases](https://mlsanity.com/paper/2309.08345/) (2023)
- [ProKD: An Unsupervised Prototypical Knowledge Distillation Network for Zero-Resource Cross-Lingual Named Entity Recognition](https://mlsanity.com/paper/2301.08855/) (2023)
- [Fine-tuned LLMs Know More, Hallucinate Less with Few-Shot Sequence-to-Sequence Semantic Parsing over Wikidata](https://mlsanity.com/paper/2305.14202/) (2023)
- [VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering](https://mlsanity.com/paper/2307.03130/) (2023)
- [RetinaQA: A Robust Knowledge Base Question Answering Model for both Answerable and Unanswerable Questions](https://mlsanity.com/paper/2403.10849/) (2024)
- [From Parse-Execute to Parse-Execute-Refine: Improving Semantic Parser for Complex Question Answering over Knowledge Base](https://mlsanity.com/paper/2305.03356/) (2023)
- [Relation-Aware Question Answering for Heterogeneous Knowledge Graphs](https://mlsanity.com/paper/2312.11922/) (2023)
- [KB-Plugin: A Plug-and-play Framework for Large Language Models to Induce Programs over Low-resourced Knowledge Bases](https://mlsanity.com/paper/2402.01619/) (2024)

## Abstract (arXiv)

Program induction for answering complex questions over knowledge bases (KBs) aims to decompose a question into a multi-step program, whose execution against the KB produces the final answer. Learning to induce programs relies on a large number of parallel question-program pairs for the given KB. However, for most KBs, the gold program annotations are usually lacking, making learning difficult. In this paper, we propose the approach of program transfer, which aims to leverage the valuable program annotations on the rich-resourced KBs as external supervision signals to aid program induction for the low-resourced KBs that lack program annotations. For program transfer, we design a novel two-stage parsing framework with an efficient ontology-guided pruning strategy. First, a sketch parser translates the question into a high-level program sketch, which is the composition of functions. Second, given the question and sketch, an argument parser searches the detailed arguments from the KB for functions. During the searching, we incorporate the KB ontology to prune the search space. The experiments on ComplexWebQuestions and WebQuestionSP show that our method outperforms SOTA methods significantly, demonstrating the effectiveness of program transfer and our framework. Our codes and datasets can be obtained from https://github.com/THU-KEG/ProgramTransfer.