Rethinking Label Smoothing on Multi-hop Question Answering (2023)

Abstract: “Multi-Hop Question Answering (MHQA) is a significant area in question answering, requiringmultiple reasoning components, including document retrieval, supporting sentence prediction,and answer span extraction. In this work, we present the first application of label smoothing tothe MHQA task, aiming to enhance generalization capabilities in MHQA systems while miti-gating overfitting of answer spans and reasoning paths in the training set. We introduce a novellabel smoothing technique, F1 Smoothing, which incorporates uncertainty into the learning pro-cess and is specifically tailored for Machine Reading Comprehension (MRC) tasks. Moreover,we employ a Linear Decay Label Smoothing Algorithm (LDLA) in conjunction with curricu-lum learning to progressively reduce uncertainty throughout the training process. Experimenton the HotpotQA dataset confirms the effectiveness of our approach in improving generaliza-tion and achieving significant improvements, leading to new state-of-the-art performance on theHotpotQA leaderboard.”

## introduction multi-hop question answering (mhqa) is a rapidly evolving research area within question answering that involves answering complex questions by gathering information from multiple sources. this requires a model capable of performing several reasoning steps and handling diverse information structures. in recent years, mhqa has attracted significant interest from researchers due to its potential for addressing real-world problems. the mainstream approach to mhqa typically incorporates several components, including a document retriever, a supporting sentence selector, and a reading comprehension module @xcite @xcite . these components collaborate to accurately retrieve and integrate relevant information from multiple sources, ultimately providing a precise answer to the given question. despite the remarkable performance of modern mhqa models in multi-hop reasoning, they continue to face challenges with answer span errors and multi-hop reasoning errors. a study by s2g @xcite reveals that the primary error source is answer span errors, constituting 74.55%, followed by multi-hop reasoning errors. this issue arises from discrepancies in answer span annotations between the training and validation sets. as illustrated in figure 1(a) , the training set answer includes the quantifier "times", while the validation set answer does not. upon examining 200 samples, we found that around 13.7% of answer spans in the hotpotqa validation set deviate from those in the training set. concerning multi-hop reasoning, we identified the presence of unannotated, viable multi-hop reasoning paths in the training set. as depicted in figure 1 (b), the non-gold document contains the necessary information to answer the question, similar to gold doc1, yet is labeled as an irrelevant document. during training, the model can only discard this reasoning path and adhere to the annotated reasoning path. given that current mhqa approaches primarily use cross-entropy loss for training multiple components, non-gold doc: f. w. woolworth building (watertown, new york) (1) the woolworth building is an historic building in watertown, new york. (2) it is a contributing building in the public square historic district. woolworth, the founder of the woolworth's chain of department stores. question: which was built first woolworth building or 1 new york plaza? answer: woolworth building evidence sentences: ["woolworth building", 0], ["1 new york plaza",0] gold doc2: 1 new york plaza (1) 1 new york plaza is an office building in new york city's financial district, built in 1969 at the intersection of south and whitehall streets. (2) it is the southernmost of all manhattan skyscrapers. figure 1 : causes of errors in answer span and multi-hop reasoning within the hotpotqa dataset. in figure (a ), the answer from the training set contains a quantifier, while the answer from the validation set does not. @xcite demonstrates that the correct answer can be inferred using a non-gold document without requiring information from gold doc1. they tend to overfit annotated answer spans and multi-hop reasoning paths in the training set. consequently, we naturally pose the research question for this paper: how can we prevent mhqa models from overfitting answer spans and reasoning paths in the training set? label smoothing is an effective method for preventing overfitting, widely utilized in computer vision @xcite . in this study, we introduce label smoothing to multi-hop reasoning tasks for the first time to mitigate overfitting. we propose a simple yet efficient mhqa model, denoted as r 3 , comprising retrieval, refinement, and reading comprehension modules. inspired by the f1 score, a commonly used metric for evaluating mrc task performance, we develop f1 smoothing, a novel technique that calculates the significance of each token within the smooth distribution. moreover, we incorporate curriculum learning @xcite and devise the linear decay label smoothing algorithm (ldla), which gradually reduces the smoothing weight, allowing the model to focus on more challenging samples during training. experimental results on the hotpotqa dataset @xcite demonstrate that incorporating f1 smoothing and ldla into the r 3 model significantly enhances performance in document retrieval, supporting sentence prediction, and answer span selection, achieving state-of-the-art results among all published works. our main contributions are summarized as follows: ## framework figure 2 depicts the overall architecture of r 3 . the retrieval module serves as the first step, where our system selects the most relevant documents, which is essential for filtering out irrelevant information. in this example, document1, document3, and document4 are chosen due to their higher relevance scores, while other documents are filtered out. once the question and related documents are given, the refinement module further selects documents based on their combined relevance. in this instance, the refinement module opts for document1 and document4. following this, the question and document1, document4 are concatenated and used as input for the reading comprehension module. within the reading comprehension module, we concurrently train supporting sentence prediction, answer span extraction, and answer type selection using a multi-task approach. ## conclusion in this study, we first identify the primary challenges hindering the performance of mhqa systems and propose using label smoothing to mitigate overfitting issues during mhqa training. we introduce f1 smoothing, a novel smoothing method inspired by the widely-used f1 score in mrc tasks. additionally, we present ldla, a progressive label smoothing algorithm that incorporates the concept of curriculum learning. comprehensive experiments on the hotpotqa dataset demonstrate that our proposed model, smoothing r 3 , achieves significant performance improvement when using f1 smoothing and ldla. our findings indicate that label smoothing is a valuable technique for mhqa, effectively improving the model's generalization while minimizing overfitting to particular patterns in the training set. in order to alleviate the complexity introduced by multiple for loops in the f1 smoothing method, we have optimized eq. ( 12 ) and eq. ( 13 ). we use l @xmath0. in equation 19 and 20, l @xmath1.