Structure-Discourse Hierarchical Graph for Conditional Question Answering on Long Documents (2023)

Abstract: Conditional question answering on long documents aims to find probable answers and identify conditions that need to be satisfied to make the answers correct over long documents. Existing approaches solve this task by segmenting long documents into multiple sections, and attending information at global and local tokens to predict the answers and corresponding conditions. However, the natural structure of the document and discourse relations between sentences in each document section are ignored, which are crucial for condition retrieving across sections, as well as logical interaction over the question and conditions. To address this issue, this paper constructs a Structure-Discourse Hierarchical Graph (SDHG) and conducts bottom-up information propagation. Firstly we build the sentence-level discourse graphs for each section and encode the discourse relations by graph attention. Secondly, we construct a section-level structure graph based on natural structures, and conduct interactions over the question and contexts. Finally different levels of representations are integrated into jointly answer and condition decoding. The experiments on the benchmark ConditionalQA shows our approach gains over the prior state-of-the-art, by 3.0 EM score and 2.4 F1 score on answer measuring, as well as 2.2 EM score and 1.9 F1 score on jointly answer and condition measuring.

## introduction conditional question answering (qa) aims to answer questions from contexts where conditions are used to distinguish answers as well as to provide additional information to support them @xcite @xcite ; sun ## related work conditional qa requires finding the probable answers and identifying their unsatisfied conditions @xcite . e 3 (zhong and zettlemoyer, 2019) extracts a set of decision rules from the context and reasons about the entailment. discern @xcite splits the document into elementary discourse units (edu) @xcite and predicts whether each edu is entailed. dgm @xcite constructs the explicit and implicit graphs of edu to capture the interactions among contexts and questions with the support of tagged discourse relationship. however, these models ignore the natural structure of documents, and the edu-based discourse graph undermines the informational continuity of sentences. moreover, simply concatenating the question with full context into a single input and encoding it with a transformer model with o(n 2 ) complexity make it not scalable to longer contexts. etc @xcite introduces attention mechanism between global tokens and regular input tokens to scale input length and encode structured inputs. dochopper @xcite utilizes the structural information that paragraphs and sentences contain different levels of information, and perform evidence retrieval at both sentence and section levels. to efficiently aggregate and combine long documents information, fid (izacard and grave, 2021) concatenates the representations of different document sections produced by the encoder independently and performs fusion in the decoder only. to enhance interaction between different levels of text segments, cgsn @xcite propagates information on the global and local graph composed of nodes for tokens, sentences as well as document sections. however, these models ignore the hierarchical structure of the document and discourse relations between sen-tences within each document section, which brings difficulty to condition locating across sections and logical reasoning for answers. hibrids @xcite injects learned biases in attention weights calculation to incorporate hierarchical document structure and produces better summaries for long documents. it shows the importance of hierarchical document structure for long document understanding. however, we highlight the section-level structural relations such as parent-child and sibling, instead of token-level path lengths and level differences on the document structure graph. ## preliminary we study the task of conditional qa over long documents (ldcqa), where the answers are only applicable when certain conditions apply. the model learns to find answers to the question from the long context and additionally performs logical reasoning over the conditions to check whether the answers are eligible. if the answers require additional conditions to be satisfied, the model identifies these unsatisfied conditions as well. formally, the input to the model includes a question @xmath0; q 2 ; • • • ; q m ] coupled with a document @xmath1; d 2 ; • • • ; d n ], where m and n denotes the length of the question and context. in our ldcqa setting, the length n can be larger than 10k. the model outputs a list of answers coupled with corresponding conditions ## methodology as shown in figure 2 , our approach includes 4 modules: plm based contextual encoder, sentencelevel discourse graph encoder, section-level structure graph encoder, fusion and decoding. first we encode each document section respectively to obtain contextual representations. then we proceed sentence interaction using parsed discourse relations for each section. then we conduct information propagation on the structure graph. finally, we integrate 3 levels of section representations with token representations to jointly generate the answers and conditions. ## analysis in this part, we do 3 ablation studies to evaluate the efficiency of 3 levels of section representations in section 4.5. then we probe our performance on long and complex documents. moreover, we explore the role of accurate document structures and discourse relations in document sections. finally, we take an example from conditionalqa dataset to show the efficiency of our structure and discourse aware hierarchical framework. ## conclusion in this paper, we propose a novel and efficient framework with hierarchical section-level structure graph and sentence-level discourse graph for conditional qa on long documents. we incorporate the natural document structure and logical discourse relations to locate answers as well as unsatisfied conditions by cross-sections retrieving and logical reasoning. we conduct experiments on the benchmark dataset in this field and our approach outperforms all the existing methods.