SRLGRN : Semantic Role Labeling Graph Reasoning Network (2020)

Abstract: This work deals with the challenge of learning and reasoning over multi-hop question answering (QA). We propose a graph reasoning network based on the semantic structure of the sentences to learn cross paragraph reasoning paths and find the supporting facts and the answer jointly. The proposed graph is a heterogeneous document-level graph that contains nodes of type sentence (question, title, and other sentences), and semantic role labeling sub-graphs per sentence that contain arguments as nodes and predicates as edges. Incorporating the argument types, the argument phrases, and the semantics of the edges originated from SRL predicates into the graph encoder helps in finding and also the explainability of the reasoning paths. Our proposed approach shows competitive performance on the HotpotQA distractor setting benchmark compared to the recent state-of-the-art models.

## introduction understanding and reasoning over natural language plays a significant role in artificial intelligence tasks such as machine reading comprehension (mrc) and question answering (qa). several qa tasks have been proposed in recent years to evaluate the language understanding capabilities of machines @xcite @xcite . these tasks are single-hop qa tasks and consider answering a question given only one single paragraph. many existing neural models rely on learning context and type-matching heuristics @xcite . those rarely build reasoning modules but achieve promising performance on single-hop qa tasks. the main reason is that these single-hop qa tasks are lacking a realistic evaluation of reasoning capabilities because they do not require complex reasoning. recently multi-hop qa tasks, such as hotpotqa @xcite and wikihop @xcite , 2018), have been proposed to assess multi-hop reasoning ability. hotpotqa task provides annotations to evaluate document level question answering and finding supporting facts. providing supervision for supporting facts improves explainabilty of the predicted answer because they clarify the cross paragraph reasoning path. due to the requirement of multi-hop reasoning over multiple documents with strong distraction, multi-hop qa tasks are challenging. figure 1 shows an example of hot-potqa. given a question and 10 paragraphs, only paragraph 1 and paragraph 2 are relevant. the second sentence in paragraph 1 and the first sentence in paragraph 2 are the supporting facts. the answer is "geelong football club". primary studies in hotpotqa task prefer to use a reading comprehension neural model @xcite @xcite . first, they use a neural retriever model to find the relevant paragraphs to the question. after that, a neural reader model is applied to the selected paragraphs for answer prediction. although these approaches obtain promising results, the performance of evaluating multi-hop reasoning capability is unsatisfac-tory @xcite . to solve the multi-hop reasoning problem, some models tried to construct an entity graph using spacy foot_0 or stanford corenlp @xcite and then applied a graph model to infer the entity path from question to the answer @xcite @xcite . however, these models ignore the importance of the semantic structure of the sentences and the edge information and entity types in the entity graph. to take the in-depth semantic roles and semantic edges between words into account here we use semantic role labeling (srl) graph as the backbone of a graph convolutional network. semantic role labeling provides the semantic structure of the sentence in terms of argument-predicate relationships @xcite . the argument-predicate relationship graph can significantly improve the multi-hop reasoning results. our experiments show that srl is effective in finding the cross paragraph reasoning path and answering the question. our proposed semantic role labeling graph reasoning network (srlgrn) jointly learns to find cross paragraph reasoning paths and answers questions on multi-hop qa. in srlgrn model, firstly, we train a paragraph selection module to retrieve gold documents and minimize distractor. second, we build a heterogeneous document-level graph that contains sentences as nodes (question, title and sentence), and srl sub-graphs including semantic role labeling arguments as nodes and predicates as edges. third, we train a graph encoder to obtain the graph node representations that incorporate the argument types and the semantics of the predicate edges in the learned representations. finally, we jointly train a multi-hop supporting fact prediction module that finds the cross paragraph reasoning path, and answer prediction module that obtains the final answer. notice that both supporting fact prediction and answer prediction are based on contextual semantics graph representations as well as token-level bert pre-trained representations. the contributions of this work are as follows: ## model description our proposed srlgrn approach is composed of paragraph selection, graph construction, graph encoder, supporting fact prediction, and answer span prediction modules. figure 2 shows the proposed architecture. in this section, we introduce our approach in detail and then explain how to train it with an efficient algorithm. ## analysis effect of srl graph. the srl graph extracts argument-predicate relationships, including indepth semantic roles and semantic edges. the constructed graph is the basis of reasoning as the inputs of each hop are directly selected from the srl graph, as shown in figure 4 . the srl graph significantly improves the completeness of the graph network, that is, providing sufficient semantic edges to cover reasoning paths, see figure 3 . compared to the ner graph in the previous models @xcite , the proposed srl graph covers the 86.5% of complete reasoning paths for the data samples. the ner graph of dfgn is incomplete and can only cover 68.7% of the reasoning paths @xcite . the graph completeness is one major reason that the srlgrn model has higher accuracy than other published models. as shown in we also observe that the f1 score of answer span prediction decreases 2.9% if we did not incorporate semantic edge information and argument types. the reason is that removing predicate edges and argument types will destroy the argument-predicate relationships in the srl graph and breaks the chain of reasoning. for example, in figure 3 , the main arguments of the two supporting facts in s 2 1 and s 2 2 (william and jerry) are connected with a predicate edge, "born", to the temporal information necessary for finding the answer. both "born" edge and the adjunct temporal roles are the key information in the two sentences to find the final answer to this question. the shared arg node, "football player", also helps to connect the line of reasoning between the two sentences. these two results indicate that both semantic roles and semantic edges in the srl graph are essential for the srlgrn performance. in a different experiment, we tested the influence of the joint training of the supporting facts and answer-prediction. as shown in effect of language models. we use two recent and widely-used pre-trained language representation models, bert and albert @xcite . the last two lines of effect of srlgrn on single-hop qa. we evaluate the srlgrn (excluding the paragraph selection module) on squad @xcite to demonstrate its reading comprehension ability. we evaluate the performance on both squad v1.1 and squad v2.0. ## error analysis synonyms are the most frequent cause of the reported errors in many cases where the predicted answer is semantically correct. as shown in the first row of the minor lexical variation (mlv) is another major cause of mistakes in the srlgrn model. as shown in the second row of paragraph selection is a small portion of errors in the srlgrn model. as shown in figure 5 , the model chooses a wrong paragraph "43rd battalion". the reason is that "43rd battalion" is a distractor although "43rd" appears in the question. the paragraph "saturday night live" is the correct relevant paragraph that includes "forty-third season" and the answer. to resolve this issue in the future, we will try to combine our model with an ir system designed for multi-hopqa similar to the multi-step entity-centric model for multi-hop qa in @xcite . comparison and bridge are two types of reasoning that are needed for answering hotpotqa questions. "bridge" reasoning predicts the answer by connecting arguments to the line of reasoning that leads to the final answer. "comparison" reasoning predicts the answer (that is, yes, no, or a text span) by comparing two arguments. srlgrn sometimes obtains wrong predictions in the "comparison" reasoning when the questions are related to "month-year" and "number". our qualitative error analysis showed that srlgrn graph leads to a wrong answer when two or more argument nodes of a same type, such as "tem-poral" type, are connected to one node in the graph. moreover, we notice that the srlgrn sometimes makes inconsistent errors. for example, in the "comparison" failing cases of figure 5 , we predict the wrong answer "wayne coyne". however, we received the correct answer after replacing the word "younger" with "older". moreover, the "bridge" type needs external knowledge in the hotpotqa task. as is shown in "bridge" failing cases of figure 5 , the selected paragraphs do not show the relation between "coker" and "miami hurricanes football team". figure 6 describes the srl construction based on this failing case. the second supporting fact and the question have the same temporal argument node "november 24, 2006". however, there is no chain between the first supporting fact and the second supporting fact due to the lack of the external knowledge that can connect "coker", "coach" and "miami hurricanes football team". therefore, the isolated reasoning chain leads to a wrong answer. ## conclusion we proposed a novel semantic role labeling graph reasoning network (srlgrn) to deal with multihop qa. the backbone graph of our proposed graph convolutional network (gcn) is created based on the semantic structure of the sentences. in creating the edges and nodes of the graph, we exploit a semantic role labeling sub-graph for each sentence and connect the candidate supporting facts. the cross paragraph argument-predicate structure of the sentences expressed in the graph provides an explicit representation of the reasoning path and helps in both finding and explaining the multiple hops of reasoning that lead to the final answer. srlgrn exceeds most of the sota results on the hotpotqa benchmark. moreover, we evaluate the model (excluding the paragraph selection module) on other reading comprehension benchmarks. our approach achieves competitive performance on squad v1.1 and v2.0.