Hop, Union, Generate: Explainable Multi-hop Reasoning without Rationale Supervision (2023)

Abstract: Explainable multi-hop question answering (QA) not only predicts answers but also identifies rationales, i. e. subsets of input sentences used to derive the answers. Existing methods rely on supervision for both answers and rationales. This problem has been extensively studied under the supervised setting, where both answer and rationale annotations are given. Because rationale annotations are expensive to collect and not always available, recent efforts have been devoted to developing methods that do not rely on supervision for rationales. However, such methods have limited capacities in modeling interactions between sentences, let alone reasoning across multiple documents. This work proposes a principled, probabilistic approach for training explainable multi-hop QA systems without rationale supervision. Our approach performs multi-hop reasoning by explicitly modeling rationales as sets, enabling the model to capture interactions between documents and sentences within a document. Experimental results show that our approach is more accurate at selecting rationales than the previous methods, while maintaining similar accuracy in predicting answers.

## introduction multi-hop reasoning is an important capability for any intelligent machine comprehension system. question answering (qa) is a common application for evaluating a system's ability to reason across multiple steps @xcite @xcite . large language models have achieved tremendous success on challenging qa tasks, even in the few-shot setting @xcite . however, @xcite and @xcite demonstrate that these models, in reality, often bypass multi-hop reasoning by performing shallow pattern matching, resulting in poor generalization ability @xcite . to avoid predictions made from such reasoning shortcuts, it is important to understand the series of steps the systems follow to derive the answers. this work explores the challenge of building explainable multi-hop qa systems, which, in addition to predicting an answer, also identify a rationale -the set of sentences that lead to the answer. depending on task specifications, the rationale can be within a single document, or span across multiple documents. explainable multi-hop qa has been extensively studied in the supervised setting, where both rationale annotations and answer annotations are given. these approaches either apply multi-task loss functions @xcite @xcite or design specialized network architectures @xcite . however, having access to rationale annotations is a strong assumption. in practice, they are expensive to collect @xcite , less available than answer annotations @xcite , and can suffer from low agreement rates between annotators @xcite . researchers have thus explored approaches that do not require rationale annotations @xcite @xcite . however, these previous approaches limit their reasoning to information from 1 or 2 sentences, and so they cannot be applied in multihop scenarios, i.e. qa tasks that require making connections between several pieces of information across sentences and across documents. additionally, these methods are either restricted to only work for multiple-choice qa, or restricted to only produce rationales at the document level but not at the sentence level. we propose hop, union, generate (hug), a principled, probabilistic approach for training explainable multi-hop qa systems without rationale supervision. hug overcomes the two-sentence limitation of previous methods by directly reasoning about rationales as sets of sentences, while also extending rationale prediction to the multidocument setting. we show an overview of hug in figure 1 . hug leverages the naturally hierarchi-emily beecham is best known for her role in a television series whose second season premiered on what date? 03/09/17 seq2seq cal structure of text and proceeds in three stagesit first selects the relevant set of documents given the question (hop); then, it selects a subset of sentences within each of the relevant documents and collects them together (union); finally, it generates an answer via a seq2seq model with all the collected sentences (generate). the key to multi-hop reasoning in hug is modeling each selection as an explicit distribution over sets. a probabilistic set distribution compares non-contiguous and variable size rationales, affording hug flexibility for rationale selection. training a set-prediction model quickly becomes intractable as the size increases. we make two algorithmic choices that lead to tractable training for hug. treating rationales as a latent variable requires hug to marginalize over all possible rationales, leading to an intractable learning objective. hug overcomes this issue by performing sampling in a hierarchical way -it first identifies the most promising documents and then the most promising sentences within those documents. second, multi-hop qa often involves reasoning over long documents, which is challenging due to the computational complexity of encoding long documents with neural models such as transformers. to make this encoding efficient, hug performs computation in the embedding space. we empirically evaluate hug on three different multi-hop qa datasets: hotpotqa @xcite , musique @xcite , fever @xcite , and mul-tirc @xcite . results show that, for both selecting rationales and predicting answers, hug is better than a number of state-of-the-art semi-supervised and unsupervised methods @xcite @xcite on all of the datasets. we also demonstrate that hug combined with larger language models consistently produces better performance. we then analyze performance according to different types of multi-hop reasoning and show that by explicitly modeling multi-hop reasoning, hug achieves a large improvement on the reasoning type that requires bridging entities. ## related work explainable methods for multi-hop qa. active research has been devoted to collecting human rationales for a wide range of qa tasks; a recent survey has identified 65 datasets that provide explanation annotations @xcite . the appearance of such datasets has enabled rapid progress in supervised methods for extracting reasoning chains; we refer readers to @xcite for a comprehensive survey. while these supervised methods such as @xcite have achieved tremendous success on retrieving rationales, even in the open-domain setting foot_0 , they can only be applied when rationale annotations are available. however, such annotations do not always exist - @xcite and @xcite propose two complex reasoning datasets that do not have rationale annotations. in these cases, we need unsupervised rationale selection methods to still build explainable qa systems. other works have explored multi-hop qa with only answer supervision but not rationale supervision. as in our work, retrieve and generate (rag) @xcite , treats the rationale as a latent variable; however, in rag the open retrieval-stage is to find a single document, ignoring the connections between different documents. rag also does not produce sentence-level rationales. other works consider sentence rationales: @xcite compute a score for every sentence pair and pick the sentence pair that has the highest score as the rationale for answer predic-tion, and @xcite perform binary classification on whether individual sentences are included in the rationale with added constraints such as consistency and faithfulness. the shared limitation of these methods is that they do not capture the dependency between more than two pieces of information. hug overcomes this limitation by performing multi-hop reasoning as document set prediction and sentence set prediction. outside of unsupervised methods, @xcite propose a semi-supervised method which collects silver rationale annotations. however, their method is limited to bridge-based questions, which is only one form of multi-hop reasoning; the other types can be found in @xcite . rationales as latent variables. a focus for rationale methods in nlp outside of multi-hop qa has been identifying subsets of input tokens to justify decisions. for text classification, @xcite , @xcite , and chen and ji (2020) frame rationales as minimal subsets of input tokens. for multi-hop qa, where input tokens are too granular a representation for rationales, treating sentences as rationales within long documents leads to the challenges of hierarchical selection and the representation of long documents; both of which we address with hug. outside of using input tokens for rationales, @xcite assume rationales take the form of unconstrained text. while flexible, this approach leads to computationally expensive training methods. therefore, we constrain rationales to be a set of sentences from given documents, which both accommodates the production of useful intermediate reasoning steps and keeps training tractable. ## generative multi-hop qa in the standard multi-hop qa setting, an example consists of a question x, a set of documents d, and an answer y. within d, some documents are relevant to the question, while the others are distractors. explainable multi-hop qa models predict a rationale z, a minimal set of sentences across the relevant documents, in addition to predicting the answer y. we show a multi-hop qa example (with distracting documents omitted) in figure 2 . ## experimental setup datasets and their representations. we evaluate hug on four multi-hop qa datasets: hot-potqa in the distractor setting @xcite , musique with answerable questions @xcite , fever @xcite , and mul-tirc @xcite . hotpotqa and musique are extractive qa datasets that require reasoning over multiple wikipedia documents and identifying a span of text as the answer. in hot-potqa, each example contains ten candidate documents, and we must identify exactly two documents (|d| = 2) that are relevant. fever is a fact checking dataset that requires verifying claims made based on wikipedia articles (|d| = 1). mul-tirc is a multiple-choice qa dataset collected from diverse sources of documents including narrative stories and news articles, and their questions can only be answered by reasoning over multiple sentences (|d| = 1). unlike conventional multichoice tasks @xcite , multirc does not pre-specify the number of correct answer choices, resulting in a more challenging setting. for fever and multirc, we consider the eraser version @xcite , where rationale annotations are made cleaner and evaluation metrics are provided. in a claim to be verified is that {x} we have following facts: {z} out the claim is thus {supported/refuted}. bqa in a claim to be verified is that steve wozniak designed homes. we have following facts: steve wozniak primarily designed the 1977 apple ii , known as one of the first highly successful mass-produced microcomputers. out the claim is thus refuted. in metrics and comparison systems. we compute f1 scores for rationale and document selection, and answer prediction. f1 scores for rationales are computed at the sentence level. because the three qa datasets are in different formats, f1 scores for answers are computed differently. for extractive qa, f1 scores are computed at the token level for the answer spans. for boolean qa and multiplechoice qa, f1 scores measure categorical answers. for each dataset, we compare to (1) state-of-the-art approaches that require no rationale supervision and (2) at least one fully supervised method (i.e., answers and rationales available for training). the latter provides an upper bound on performance. we also consider a semi-supervised approach -chain @xcite ; they assume no access to gold rationale annotations and supervise their model on silver rationales produced with external entity taggers. the fully supervised system we consider is sae @xcite . both chain and sae use roberta-large as sentence encoders. implementation and hyperparameters. we test hug with language models of both small and large sizes. for the small version (hug-small), we use distilbert @xcite as the encoder and bart-base as the seq2seq model. for the large version (hug), we use roberta-large as the encoder and bart-large as the seq2seq model. we only test rag in the small version. we implement hug with hugging face transformers @xcite . we perform grid search with learning rates {5e-6, 1e-5, 2e-5} and batch sizes {2,4,8,16} for both hug and rag. we train our system for 3 epochs for hotpotqa and 5 epochs for the other three datasets. we warm up the learning rate with first 10% examples. we choose the checkpoint that has the highest answer f1 score on the validation set. we consider rationales up to four sentences for hotpotqa and rationales up to three sentences for the other datasets. finally, we take s 10 and s 9 ## results hotpotqa. we summarize the results on hot-potqa in ## analysis document dependencies hug explicitly models the dependencies between documents for multi-hop reasoning. we consider independent document selection foot_1 to see whether this dependencies is necessary on the hotpotqa dataset. to understand how document modeling impact rationale selection performance, we break the performance down by the reasoning types proposed in @xcite : comparison-based reasoning and bridge-based reasoning. in comparison-based reasoning, relevant documents independently contribute to the answer, whereas for bridge-based reasoning, relevant documents require connections to previously selected documents. ## conclusion we present hug, a probabilistic, principled approach for explainable multi-hop reasoning without rationale supervision. hug explicitly models multi-hop reasoning by considering the dependency between documents and between sentences within a document. experimental results demonstrate that hug outperforms other state-of-the-art methods that do not rely on rationale labels.