Semantic parsing maps natural language questions into logical forms, which can be executed against a knowledge base for answers. In real-world applications, the performance of a parser is often limited by the lack of training data. To facilitate zero-shot learning, data synthesis has been widely studied to automatically generate paired questions and logical forms. However, data synthesis methods can hardly cover the diverse structures in natural languages, leading to a large gap in sentence structure between synthetic and natural questions. In this paper, we propose a decomposition-based method to unify the sentence structures of questions, which benefits the generalization to natural questions. Experiments demonstrate that our method significantly improves the semantic parser trained on synthetic data (+7.9% on KQA and +8.9% on ComplexWebQuestions in terms of exact match accuracy). Extensive analysis demonstrates that our method can better generalize to natural questions with novel text expressions compared with baselines. Besides semantic parsing, our idea potentially benefits other semantic understanding tasks by mitigating the distracting structure features. To illustrate this, we extend our method to the task of sentence embedding learning, and observe substantial improvements on sentence retrieval (+13.1% for Hit@1).

/pdf/bridging-the-gap-between-synthetic-and-natural-questions-via-1nepl0m0.pdf

Bridging the Gap between Synthetic and Natural Questions via Sentence Decomposition for Semantic Parsing

To explain the predicted answers and evaluate the reasoning abilities of models, several studies have utilized underlying reasoning (UR) tasks in multi-hop question answering (QA) datasets. However, it remains an open question as to how effective UR tasks are for the QA task when training models on both tasks in an end-to-end manner. In this study, we address this question by analyzing the effectiveness of UR tasks (including both sentence-level and entity-level tasks) in three aspects: (1) QA performance, (2) reasoning shortcuts, and (3) robustness. While the previous models have not been explicitly trained on an entity-level reasoning prediction task, we build a multi-task model that performs three tasks together: sentence-level supporting facts prediction, entity-level reasoning prediction, and answer prediction. Experimental results on 2WikiMultiHopQA and HotpotQA-small datasets reveal that (1) UR tasks can improve QA performance. Using four debiased datasets that are newly created, we demonstrate that (2) UR tasks are helpful in preventing reasoning shortcuts in the multi-hop QA task. However, we find that (3) UR tasks do not contribute to improving the robustness of the model on adversarial questions, such as sub-questions and inverted questions. We encourage future studies to investigate the effectiveness of entity-level reasoning in the form of natural language questions (e.g., sub-question forms). 

pdf/analyzing-the-effectiveness-of-the-underlying-reasoning-508pazdiic.pdf

Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering

Neural models, including large language models (LLMs), achieve superior performance on multi-hop question-answering. To elicit reasoning capabilities from LLMs, recent works propose using the chain-of-thought (CoT) mechanism to generate both the reasoning chain and the answer, which enhances the model’s capabilities in conducting multi-hop reasoning. However, several challenges still remain: such as struggling with inaccurate reasoning, hallucinations, and lack of interpretability. On the other hand, information extraction (IE) identifies entities, relations, and events grounded to the text. The extracted structured information can be easily interpreted by humans and machines (Grishman, 2019). In this work, we investigate constructing and leveraging extracted semantic structures (graphs) for multi-hop question answering, especially the reasoning process. Empirical results and human evaluations show that our framework: generates more faithful reasoning chains and substantially improves the QA performance on two benchmark datasets. Moreover, the extracted structures themselves naturally provide grounded explanations that are preferred by humans, as compared to the generated reasoning chains and saliency-based explanations. 

pdf/leveraging-structured-information-for-explainable-multi-hop-2uzysnb2c2.pdf

Leveraging Structured Information for Explainable Multi-hop Question Answering and Reasoning

We propose Chain-of-Questions, a framework that trains a model to robustly answer multistep questions by generating and answering sub-questions. We obtain supervision for sub-questions from human-annotated question decomposition meaning representation (QDMR), but QDMR does not include annotated answers to sub-questions. To overcome this technical challenge, we treat sub-answers as latent variables and infer them with a novel dynamic mixture of Hard-EM and MAPO. Chain-of-Questions is effective and robust, greatly outperforming strong neuro-symbolic methods by 9.0 F1 on a DROP contrast set and GPT-3.5 by 24.3 F1 on a HotpotQA adversarial set. 

pdf/chain-of-questions-training-with-latent-answers-for-robust-1iowawyafj.pdf

Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering

Successful few-shot question-answering with large language models (LLMs) has been reported for a variety of tasks. In the usual approach, an answer is generated by a single call to an LLM, but it has been pointed out that the performance of multi-hop inference by LLMs is not sufficient. Thus, an LLM is unable to perform the complex processing necessary to get an answer, which leads to poor performance. Moreover, the inference process is opaque. Against this, approaches that call an LLM multiple times have been proposed, but many of these approaches can only be used for a limited number of effective tasks, and LLMs essentially require complex processing.To address these problems, we propose the Exploratory Inference Chain (EIC) framework that combines the implicit processing of LLMs with explicit inference chains, and this is based on the dual process theory of human cognitive processes. The EIC framework first generates the information needed to answer a multi-hop question as keywords and then performs 1-hop inference for each keyword. If the inference is not sufficient, additional inferences are performed. This process is repeated, and when sufficient inferences are obtained, they are aggregated, and the final answer is generated. This makes the information per inference by LLM simplified, and logical inference is achieved through an explicit inference chain.We conducted experiments on two multi-hop QA datasets and confirmed through a quantitative evaluation that our EIC framework performed better than existing approaches. Moreover, a qualitative evaluation confirmed that our approach can effectively perform inference so as to get closer to the answer in question-answering tasks that require knowledge. In addition, compared with existing approaches, the EIC framework improves the interpretability of the output. 

Exploratory Inference Chain: Exploratorily Chaining Multi-hop Inferences with Large Language Models for Question-Answering

Multi-hop QA requires the machine to answer complex questions through finding multiple clues and reasoning, and provide explanatory evidence to demonstrate the machine’s reasoning process. We propose Relation Extractor-Reader and Comparator (RERC), a three-stage framework based on complex question decomposition. The Relation Extractor decomposes the complex question, and then the Reader answers the sub-questions in turn, and finally the Comparator performs numerical comparison and summarizes all to get the final answer, where the entire process itself constitutes a complete reasoning evidence path. In the 2WikiMultiHopQA dataset, our RERC model has achieved the state-of-the-art performance, with a winning joint F1 score of 53.58 on the leaderboard. All indicators of our RERC are close to human performance, with only 1.95 behind the human level in F1 score of support fact. At the same time, the evidence path provided by our RERC framework has excellent readability and faithfulness.

Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable

Multi-hop QA requires the machine to answer complex questions through finding multiple clues and reasoning, and provide explanatory evidence to demonstrate the machine reasoning process. We propose Relation Extractor-Reader and Comparator (RERC), a three-stage framework based on complex question decomposition, which is the first work that the RERC model has been proposed and applied in solving the multi-hop QA challenges. The Relation Extractor decomposes the complex question, and then the Reader answers the sub-questions in turn, and finally the Comparator performs numerical comparison and summarizes all to get the final answer, where the entire process itself constitutes a complete reasoning evidence path. In the 2WikiMultiHopQA dataset, our RERC model has achieved the most advanced performance, with a winning joint F1 score of 53.58 on the leaderboard. All indicators of our RERC are close to human performance, with only 1.95 behind the human level in F1 score of support fact. At the same time, the evidence path provided by our RERC framework has excellent readability and faithfulness.

Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable.

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Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable

Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable.