Single Sequence Prediction over Reasoning Graphs for Multi-hop QA (2023)

Abstract: Recent generative approaches for multi-hop question answering (QA) utilize the fusion-in-decoder method to generate a single sequence output which includes both a final answer and a reasoning path taken to arrive at that answer, such as passage titles and key facts from those passages. While such models can lead to better interpretability and high quantitative scores, they often have difficulty accurately identifying the passages corresponding to key entities in the context, resulting in incorrect passage hops and a lack of faithfulness in the reasoning path. To address this, we propose a single-sequence prediction method over a local reasoning graph that integrates a graph structure connecting key entities in each context passage to relevant subsequent passages for each question. We use a graph neural network to encode this graph structure and fuse the resulting representations into the entity representations of the model. Our experiments show significant improvements in answer exact-match/F1 scores and faithfulness of grounding in the reasoning path on the HotpotQA dataset and achieve state-of-the-art numbers on the Musique dataset with only up to a 4% increase in model parameters.

## introduction multi-hop question answering (qa) involves reasoning over multiple passages and understanding the relationships between those pieces of information to answer a question. compared with singlehop qa, which often extracts answers from a single passage, multi-hop qa is more challenging as it requires a model to determine the relevant facts from multiple passages and connect those facts for reasoning to infer the final answer. to tackle multi-hop qa, recent works have investigated large pretrained generative models (lewis figure 1 : localized graph construction connecting entity spans to corresponding passages in the context. if there are multiple passages with the same title, we connect the entity span to all such passages. @xcite @xcite and demonstrated their effectiveness over traditional extractive models @xcite . compared with extractive models, the ability of generative models to effectively aggregate and combine evidence from multiple passages proves advantageous for multi-hop qa. in particular, @xcite propose a method called fid @xcite , which leverages passage retrieval with a generative model, such as t5 @xcite or bart @xcite , to achieve state-of-the-art performance on various single-hop qa tasks. however, this approach does not extend well to multi-hop qa tasks @xcite , as it sorely relies on a black-box generative model to generate answers directly without explicitly modeling the multi-hop reasoning process. additionally, fid encodes multiple context passages independently for multi-hop qa, ignoring the structural and semantic relationship between these passages @xcite . building on fid, path-fid @xcite addresses the interpretability issue by training a model to generate a reasoning path that contains supporting pas-sage titles, facts, and the final answer. however, our analysis of path-fid outputs shows disconnected reasoning with incorrect passage hops in the model's reasoning path which affects final answer generation. recently, there have been multiple techniques @xcite @xcite to counter disconnected reasoning which operate at the dataset level, using adversarial training, adding extra annotations or using dataset rebalancing for training. while these approaches optimize models to mitigate disconnected reasoning @xcite , the performance on the original test set often suffers from a significant decrease. in this paper, we propose a single-sequence prediction method over a local reasoning graph (seqgraph) that integrates a graph structure connecting key entities in each context passage to relevant subsequent passages for each question. different from the prior works, our method not only mitigates the disconnected reasoning issue but also maintains robust performance on the original dataset. intuitively, for each multi-hop question, our method leverages the structural relationship between different passages to learn structured representations through a graph neural network (gnn) @xcite . the structured representations are fused to bias the generative model toward predicting a faithful, connected reasoning path which improves answer predictions. our experiments on the hotpot-qa dataset @xcite show clear improvements in exact-match(em)/f1 scores compared to generative baselines in the distractor setting while minimizing disconnected reasoning quantified by the dire score @xcite . we also achieve the state-of-the-art performance on the musique-answerable test dataset @xcite with a 17-point improvement in answer f1 over the current best-performing model in the end-to-end (e2e) category. to summarize, our contributions are as follows: ## preliminaries problem setup: in a multi-hop qa task, each qa pair in a labeled dataset d is given along with a set of n passages, p @xmath0, p 2 , ..., p n }, i.e., (q, a, p q ) ∈ d, where a passage has its title and content p @xmath1, c i ). the task is to learn a model parameterized θ to generate an answer string a for the given question q and p q . in this paper, we focus on the distractor setting, where p q is given for each question and contains m distractors that are not useful to the answer prediction. thus, this task requires a model to reason over multiple hops of the remaining nm relevant passages. in addition to predicting the final answer a, we also aim to train a model to predict a reasoning path r of important elements (e.g., relevant passage titles, supporting facts in a passage) that lead to the final answer. ## method in this section, we describe our proposed method for solving disconnected reasoning for multi-hop qa in the distractor setting. overview: our method first constructs a local graph over passage contexts for each question ( §3.1), and integrates the graph information with the key entities to improve the generation of reasoning paths ( §3.2). different from prior works that encode all the passages independently, we connect the passages through the key pivot entities into a local graph for a question, which allows us to encode structural representations across passages by a graph neural network. these graph structured representations are then fused with the contextualized text representations from a text encoder, guiding the model to leverage structural information to alleviate disconnected reasoning over passages. ## experimental setting in this section, we elaborate on the datasets, the baseline models and the variants of seqgraph we consider for our experiment settings. we consider two multi-hop qa datasets, hotpot-qa and musique. since seqgraph is primarily focused only on improving the efficacy of encoding, we consider only the distractor setting for both datasets. hotpot-qa: the final answer to each question in the distractor setting is extracted from 10 passages. the dataset includes two main types of questions: bridge (80%) and comparison (20%). bridge questions often require identifying a bridge entity in the first passage to correctly hop to the second passage that contains the answer, while comparison questions do not have this requirement. each question is also provided with annotations of 2 supporting passages (2-hop) and up to 5 corresponding relevant sentences as their supporting facts. musique: musique has questions that range in difficulty from 2 to 4-hops and six types of reasoning chains. musique uses a stringent filtering process as well as a bottom-up technique to iteratively combine single-hop questions from several datasets into a k-hop benchmark that is more difficult than each individual dataset and significantly less susceptible to the disconnected-reasoning problem. unlike hotpot-qa, musique does not provide annotations of relevant sentences but provides supporting passage titles, question decomposition(decomposition of a multi-hop question into simpler 1-hop sub-questions) and also intermediate answers to the decomposed questions. given this variety, we use the following reasoning path variants to train the model to generate: ## related works multihop question answering requires a model to perform reasoning over multiple pieces of information, utilizing multiple sources and inferring relationships between them to provide a correct answer to a given question. there have been various approaches and datasets proposed for training qa systems, such as hotpotqa @xcite , iirc @xcite and musique @xcite . in the hotpot-qa full-wiki setting, the task is to find relevant facts from all wikipedia articles and then use them to complete the multi-hop qa task. retrieval models play an important role in this setting, such as dpr @xcite , which focuses on retrieving relevant information in the semantic space. other methods, such as entitiescentric @xcite , and golden retriever @xcite , use entities mentioned or reformulated in query keywords to retrieve the next hop document. additionally, pathretriever @xcite and hopretriever @xcite use rnn to select documents to form a paragraph-level reasoning path iteratively. the above methods mainly focus on the open-domain setting (full-wiki) and improve the retriever's performance and do not address the disconnected reasoning problem. multiple techniques @xcite @xcite to counter disconnected reasoning operate at the dataset level, using adversarial training, adding extra annotations or using dataset augmentations to get a balanced train set and prevent the model from cheating. we highlight differences between our approach and other related works on hotpot-qa-distractor and other works that combine language models with graphs below : generative approaches: our generative-fid approach differs from others using kg/gnn @xcite as we use an entity-passage graph with wikipedia hyperlinks. also, our focus is primarily on the distractor setting of multi-hop qa, while other baselines @xcite are either single-hop or improving retrieval in open-domain setting pipeline vs single-stage: other baselines @xcite @xcite @xcite use a pipeline approach with distinct encoder models in the reasoning process, while we use a single-stage, oneshot prediction process without assumptions on the number of hops. graph construction: other methods @xcite select relevant passages heuristically from among distractors to construct graphs. however, we construct our entity-passage graph on all passages (including distractors) and fuse the representations in the encoder. while a direct comparison with pipeline-based approaches is not possible or fair, we provide comparisons in ## conclusion in this paper, we propose seqgraph, an approach that utilizes the structured relationship between passages in the context of multi-hop questions to reduce disconnected reasoning. we construct a localized entity-passage graph using wikipedia hyperlinks, encode it using a gnn, and fuse the structured representations with the text encoder for predicting a reasoning path. our approach results in strong performance gains in terms of both answer and support em/f1 on hotpot-qa and reduces disconnected reasoning measured using dire score. we also obtain state-of-the-art performance on the more challenging musique benchmark with a 17-point improvement in answer f1 over the current best end-to-end(e2e) model. experimenting with sophisticated methods of encoding the graph structure and fusing the text and graph representations can be explored in future work.