Explainable Question Answering based on Semantic Graph by Global Differentiable Learning and Dynamic Adaptive Reasoning (2022)

Abstract: Multi-hop Question Answering is an agent task for testing the reasoning ability. With the development of pre-trained models, the implicit reasoning ability has been surprisingly improved and can even surpass human performance. However, the nature of the black box hinders the construction of explainable intelligent systems. Several researchers have explored explainable neural-symbolic reasoning methods based on question decomposition techniques. The undifferentiable symbolic operations and the error propagation in the reasoning process lead to poor performance. To alleviate it, we propose a simple yet effective Global Differentiable Learning strategy to explore optimal reasoning paths from the latent probability space so that the model learns to solve intermediate reasoning processes without expert annotations. We further design a Dynamic Adaptive Reasoner to enhance the generalization of unseen questions. Our method achieves 17% improvements in F1-score against BreakRC and shows better interpretability. We take a step forward in building interpretable reasoning methods.

## introduction multi-hop question answering involves retrieving supporting facts from multiple documents along with the explicit reasoning path and reasoning out the answer @xcite . as pre-trained language models evolved, the performance on this task improved spectacularly @xcite @xcite @xcite . despite the success, the black-box nature of pure neural networks has raised concerns among researchers that the unexplainable reasoning process is unacceptable for building trustworthy and robust intelligent systems @xcite ; (a) gives an instance of the question semantic graph. as (c) shows, we propose two simple yet effective improvements for the explainable reasoning method illustrated in (b), including global differentiable learning and dynamic adaptive reasoner (da-reasoner). @xcite @xcite . a feasible way to realize an explainable reasoning mechanism is by modeling the reasoning path explicitly. some researchers have successfully explored the idea of breaking up a multi-hop question into sub-questions and solving them step by step according to the logical relationships to arrive at the final answer. due to the complexity and expense of constructing question decomposition datasets, early work explored unsupervised @xcite or weakly supervised @xcite question decomposition methods. however, the sub-questions lack reasoning over logical relationships, thus they are only valuable for retrieving supporting facts. as figure 1 (a) shows, the allen institute @xcite proposed the first large-scale question decomposition dataset, where each instance contains a multi-hop question and a question semantic graph consisting of sub-questions annotated by human experts according to a reasoning path. based on this, they further explored breakrc, a neural-symbolic reasoning method, and achieved good interpretability. however, the undifferentiable symbolic operations make the neural network reasoner untrainable. thus, the semantic space of the reasoner does not match the target sub-questions. furthermore, the error propagation in the reasoning path exacerbates this effect leading to the performance lagging behind the mainstream implicit reasoning models. we propose a simple yet effective global differentiable learning strategy to alleviate the problem, as is shown in figure 2 . it learns reasoning capability by exploring the optimal reasoning path in the latent reasoning space. the reasoner will predict a set of candidate answers for each sub-question one by one. then the answer will be sampled by probability and passed to the answer slots in the next logically adjacent sub-question. during training, for the same instance, the model explores a variety of reasoning paths in the potential space by probability. we let the gradients backpropagated under symbolic operations by using the straight-through estimator @xcite . the trick allows the reasoner to become trainable to adapt to sub-questions without ground-truth answers. we further design an dynamic adaptive reasoner to improve generalization to unseen sub-questions. ## method this section first introduces the backbone network, including question decomposition and neuralsymbolic reasoning mechanism. then we introduce the proposed two improvements. backbone figure 1 illustrates the architecture of breakrc. given a multi-hop question q, it first uses a decomposition model @xcite to break out a multi-hop question into a set of sub-questions @xmath0, ..., {sq n ;#n-1;#n-2}⟩. each sub-question contains zero or several answer slots. the slot number corresponds to the subquestion number, meaning that slot #n should be filled with the answer to the nth sub-question sq n . a directed acyclic question semantic graph can be constructed based on the slot relationship. reasoner r is an off-the-shelf single-hop reading com- da-reasoner sub-question #1 da-reasoner sub-question #2 da-reasoner sub-question #4 final answer differentiable sampling by probability confidence calculation da-reasoner sub-question #3 gumbel nosie softmax with temperature argmax (detached) latent reasoning space & the sampled reasoning path + answer 2 selected candidated answers of sub-question #1 slot #1 slot #2 slot #3 a 1 a 2 a 3 a 4 a 1 a 1 a 1 a 1 a 1 a 1 a 1 e 1 l 1 ground-truth answer of multi-hop question cross-entropy function gradient back propagation overview 1 2 3 4 k … prehension model. it predicts answers {a 1 , ..., a n } to the sub-questions {s 1 , ..., s n } based on context c one by one and fills the corresponding answer slots. the answer to the last sub-question sq n is used as the reasoning result of the original multihop question. θ is set of the model parameters. global differentiable learning figure 2 illustrates the learning process. it learns to solve intermediate reasoning process by exploring various reasoning paths from the latent reasoning space through a differentiable sampling strategy, alleviating the problem of semantic space mismatch and error propagation. due to the lack of ground-truth answers for the sub-questions, we assume that all the candidate answers are possible correct options. different reasoning paths are generated when different sub-answers are passed over the question semantic graph. all possible paths form a large latent reasoning space. specifically, the reasoner r takes the sub-question sq n for which the slot has been filled and the context c as input, and predicts k candidate answers each answer is a successive token span in the context. the confidence e @xmath1, ..., e n k } of each answer is the sum of the probabilities of the first and the last token being the span's start and end points, respectively. we reparameterize the confidence e @xmath2: we then apply softmax with temperature τ to calculate the logits l @xmath3, ..., l n k } for the reparameterized confidence e n . finally, we sample answer by applying argmax function. the above process achieves probability-based sampling. the sampled answer is passed to the slot in the corresponding sub-question. repeat the above process until the last sub-question. the final reasoning result is the predicted answer to the last sub-question. first, we use the crossentropy function to measure the difference between it and the ground-truth answer to the original multihop question. then, we use a straight-through estimator @xcite to detach the undifferentiable discrete operation argmax from the computational graph. it makes it possible to backpropagate the gradient along the reasoning path. the reasoner learns to solve the intermediate reasoning process by performing gradient updating. ## related work many early works focused on improving information retrieving and implicit reasoning mechanism @xcite @xcite @xcite @xcite @xcite @xcite @xcite @xcite . despite the success, they are unexplainable. various interpretable methods have been proposed for hotpotqa. de-comprc @xcite explored a weakly supervised question decomposition method and ensembles the results of the question decompositionbased and implicit reasoning methods. cogqa @xcite built a cognitive graph by coordinating an implicit extraction module and an explicit reasoning module to provide explainable reasoning paths. snmn @xcite leveraged the neural module network to construc explainable system. modularqa @xcite learns to ask sub-questions to existing simple qa models without annotated decompositions. breakrc @xcite constructed the first large-scale question decomposition dataset and proposed a novel neural-symbolic reasoning method that shows good interpretability. ## experiments datasets we evaluate our method on both the distractor and fullwiki settings of hotpotqa @xcite . the dataset contains 105,257 multihop questions derived from wikipedia paragraphs, where the correct answer is a span in these paragraphs. we present the em (exact match) and f1 scores. implementation details for global differentiable learning, we set the temperature τ to 10 and halve it after each epoch. for dynamic adaptive reasoner, we choose the last four layers of the encoder as the dynamic adaptive part, with the number of blocks per layer set to 3. we follow the same approach of breakrc to use the bert-based rc model from @xcite as the basic reasoner, trained solely on squad @xcite (a single-hop question answering dataset). for optimization, we use adam and set the learning rate to 2e-5. the dimension of handle features is set to 768. the neuron number of reasoning type embedding layer is set to 768. the maximum number of epochs is set to 5. we conduct our experiments on nvidia v100 gpu with 32gb memory. ## conclusion we take a step forward in constructing the explainable method for multi-hop question answering by proposing two effective improvements. the global differentiable learning strategy learns optimal reasoning paths by exploring latent probability space to alleviate the problem of semantic space mismatch and error propagation. the dynamic adaptive reasoner improves generalization to unseen sub-questions. case 1: [question]: where does the state legislature in which john coffin talbot served convene? [answer]: the state house in augusta [context]: ... talbot, a democrat, served 6 one-year terms in the maine legislature (correct answer of sub-question #1), including two in the maine house of representatives (1825; 1831) ... the legislature convenes at the state house in augusta (final answer), where it has met since 1832. … [results of reasoning on question semantci graph]: state legislature in which john coffin talbot served. where #1 convenes #1 #2 select (reasoning type) bridge [predicted answer of sub-question #1]: breakrc: california state legislature ours: maine legislature [predicted answer of sub-question #2]: breakrc: each new two-year session ours: state house in augusta (as final answer) pass answer to slot #1 case 2: @xcite : what is the proper name for the steroid used by the retired british track sprinter who is one of the fastest european sprinters in the history of athletics?