A Transition-based Method for Complex Question Understanding (2022)

Abstract: Complex Question Understanding (CQU) parses complex questions to Question Decomposition Meaning Representation (QDMR) which is a sequence of atomic operators. Existing works are based on end-to-end neural models which do not explicitly model the intermediate states and lack interpretability for the parsing process. Besides, they predict QDMR in a mismatched granularity and do not model the step-wise information which is an essential characteristic of QDMR. To alleviate the issues, we treat QDMR as a computational graph and propose a transition-based method where a decider predicts a sequence of actions to build the graph node-by-node. In this way, the partial graph at each step enables better representation of the intermediate states and better interpretability. At each step, the decider encodes the intermediate state with specially designed encoders and predicts several candidates of the next action and its confidence. For inference, a searcher seeks the optimal graph based on the predictions of the decider to alleviate the error propagation. Experimental results demonstrate the parsing accuracy of our method against several strong baselines. Moreover, our method has transparent and human-readable intermediate results, showing improved interpretability.

## introduction the task of complex question understanding (cqu) aims at converting complex questions which require multi-hop reasoning into consecutive trivial sub-questions. an example of cqu is shown in figure 1 . to answer the question "return me the author in the university of michigan whose papers have more than 5000 total citations", cqu models decompose it into several trivial sub-questions (e.g. "return authors"), and the final answer is obtained by consecutively answering the sub-questions. to capture the meaning of questions over unstructured sources such as text and images, question decomposition meaning representation (qdmr) @xcite is proposed where questions are represented through a sequence of atomic executable operators, and the final answer can be obtained by answering the operator sequences in order. qdmr has been shown to improve the performance and interpretability for multi-hop question answering @xcite @xcite . existing works for cqu can be roughly divided into two categories: the seq2seq-based autoregressive parser @xcite and the dependency-based non-autoregressive parser @xcite . however, these approaches are based on end-to-end models which do not explicitly model the intermediate states and lack interpretability for the parsing process. besides, they predict qdmr in a mismatched granularity. the former category generates qdmr as a sentence (as shown in figure 1 ➊ ) and adopts the seq2seq model to decode the qdmr token-by-token. this tokenlevel modeling is sub-optimal since it ignores the inherent operator-level structure of qdmr and thus performs worse when the qdmr has longer operators and when the question is informative. the latter category maps qdmr to a dependency graph over the question tokens (as shown in figure 1 ➋ ) and adopts a non-autoregressive parser to decode the entire dependency graph in a single step. it predicts all operators in the qdmr simultaneously and ignores the interaction between operators, trading off performance for computational efficiency. @xcite also tries combining the two approaches by exploiting the graph supervision to train the encoder in the seq2seq model. to sum up, these methods have drawbacks in modeling the intermediate states and the step-wise information which is a distinct characteristic of qdmr. to alleviate the shortcomings of the above methods while preserving their advantages, we treat qdmr as a computational graph and propose a transition-based method where a decider predicts a sequence of actions to build the graph node-bynode. at each transition step, one new node and its referencing edges are decided given the question and the previously generated partial graph. in this way, the partial graph at each step enables better representation of the intermediate states and better interpretability. we illustrate the proposed method in fig. 2 . the generated graph starts from empty and expands incrementally in a node-by-node manner. at each step, given the question and the current state, the step-wise decider encodes them with specially designed encoders and predicts several candidates for the next action which includes a node, its connecting edges, and its confidence. after each step, the partial graph is either expanded according to the action or finalized as a qdmr. for inference, a searcher seeks the optimal graph based on the predictions of the decider to alleviate the error propagation. to verify the effectiveness of our proposed method, abundant experiments are conducted on the break dataset, which contains 83,978 examples from ten qa datasets across three modalities. experimental results show that our method outperforms strong baselines and achieves the state-ofthe-art on the break dataset. we further analyze the interpretability of our method. overall, our work makes the following major contributions: ## conclusion in this paper, to model the intermediate states and the step/operator-wise semantic, we view qdmr as a computational graph and propose a transitionbased method where a decider wrapped with a searcher incrementally constructs the graph. experimental results show that our framework outperforms the state-of-the-art cqu model by 3.1 normem and 2.0 lf-em. further visualization also demonstrates the interpretability of our method by giving transparent and human-readable intermediate results.