Red Dragon AI at T ext G raphs 2020 Shared Task : LIT : LSTM -Interleaved Transformer for Multi-Hop Explanation Ranking (2020)

Abstract: Explainable question answering for science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. To counter the limitations of methods that view each query-document pair in isolation, we propose the LSTM-Interleaved Transformer which incorporates cross-document interactions for improved multi-hop ranking. The LIT architecture can leverage prior ranking positions in the re-ranking setting. Our model is competitive on the current leaderboard for the TextGraphs 2020 shared task, achieving a test-set MAP of 0.5607, and would have gained third place had we submitted before the competition deadline. Our code implementation is made available at [ https://github.com/mdda/worldtree_corpus/tree/textgraphs_2020 ]( https://github.com/mdda/worldtree_corpus/tree/textgraphs_2020 ).

## discussion other architectures that we explored included graph neural network (gnn) methods, however we had insufficient time to tune these for the multi-hop explanation task herein. surprisingly, our simple lstm methods (which can be viewed as a linear graph that performs message-passing along the list of results ordered by the i-bm25 method) already provided a competitive method. we estimate that next year's competition will require the use of graph-based methods, due to their greater expressive power. ## conclusion the lit architecture is a simple yet powerful adaptation of the transformer architecture to learn better cross-document interactions for multi-hop ranking. the structure can be easily integrated with any transformer language model to enable cross-referencing of knowledge statements and improved ranking performance. for example, lit can be a drop-in encoder for other multi-hop question answering datasets such as hotpotqa @xcite . when applied to the challenging worldtree v2 dataset, lit achieves competitive performance with current state-of-the-art models despite a smaller footprint. we envision that this architecture can be beneficial to many nlp tasks which require multi-hop reasoning over documents.