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.
Commonsense generation is a challenging task of generating a plausible sentence describing an everyday scenario using provided concepts. Its requirement of reasoning over commonsense knowledge and compositional generalization ability even puzzles strong pre-trained language generation models. We propose a novel framework using retrieval methods to enhance both the pre-training and fine-tuning for commonsense generation. We retrieve prototype sentence candidates by concept matching and use them as auxiliary input. For finetuning, we further boost its performance with a trainable sentence retriever. We demonstrate experimentally on the large-scale Common-Gen benchmark that our approach achieves new state-of-the-art results. 1 * Work done during internship at Microsoft. 1 The code and data are available at https://github. com/HanNight/RE-T5 Concept Set #1: dog, frisbee, catch, throw Gold Target Sentences: A dog leaps to catch a thrown frisbee. The dog catches the frisbee when the boy throws it. A man throws away his dog 's favorite frisbee expecting him to catch it in the air. Concept Set #2: lake, shore, canoe Gold Target Sentences: Canoe on a shore of lake. Canoe on shore with rainbow across the lake. Several canoes parked in the grass on the shore of a lake.
Pre-trained language models such as BERT have achieved the state-of-the-art performance on natural language inference (NLI). However, it has been shown that such models can be tricked by variations of surface patterns such as syntax. We investigate the use of dependency trees to enhance the generalization of BERT in the NLI task, leveraging on a graph convolutional network to represent a syntax-based matching graph with heterogeneous matching patterns. Experimental results show that, our syntax-based method largely enhance generalization of BERT on a test set where the sentence pair has high lexical overlap but diverse syntactic structures, and do not degrade performance on the standard test set. In other words, the proposed method makes BERT more robust on syntactic changes. * Equal Contribution. Work is done when working at Westlake University.
In this study, we investigated the dynamic properties of oscillatory activities in the scalp electro-encephalographs (EEGs) of 20 participants involved in a novel dynamic manipulating task using a physical interface and a virtual feedback. The complexity of such a task a rises from the unexpected relationship between the magnitude of the motion and the feedback. The characterization of complex patterns arising from EEG is an important problem in identifying different mental intentions. We proposed a scaling analysis of phase fluctuation in the scalp EEG to discriminate the network states related to different EEG patterns, which correspond to manipulating the task with right or left movement intention. These intentions are generated while the participant is engaged in such a complex task. The phase characterization method was used to calculate the instantaneous phase from the operational EEG. Then, functional brain networks (FBNs) of 20 subjects based on the task-related EEG were constructed by phase synchronization. The degree features representing the structures and scaling components of brain networks are sensitive to the EEG patterns with left or right motor intention. The correlation between features and mental intentions was investigated by discriminant analysis. For 20 subjects, the average accuracy of state detection is [Formula: see text], and the average mean-squared error (MSE) is [Formula: see text]. The brain state depicted by the results is related to high awareness, the phase characterization is of the effectiveness in EEG processing and FBN construction and the difference of control intentions can be explored by the phase characterization method. This finding may be relevant to understanding some neuronal mechanisms underlying the attention and some applications of closed-loop control for the safety operation of tools.
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