R3 : Refined Retriever-Reader pipeline for Multidoc2dial (2022)

Abstract: In this paper, we present our submission to the DialDoc shared task based on the MultiDoc2Dial dataset. MultiDoc2Dial is a conversational question answering dataset that grounds dialogues in multiple documents. The task involves grounding a user’s query in a document followed by generating an appropriate response. We propose several improvements over the baseline’s retriever-reader architecture to aid in modeling goal-oriented dialogues grounded in multiple documents. Our proposed approach employs sparse representations for passage retrieval, a passage re-ranker, the fusion-in-decoder architecture for generation, and a curriculum learning training paradigm. Our approach shows a 12 point improvement in BLEU score compared to the baseline RAG model.

## introduction the task framework of document-grounded, conversational question answering unifies several closely related task frameworks, including open-domain question answering (qa), conversational qa, and knowledge-grounded generation. in open-domain question answering tasks, such as squad @xcite , models are required to respond to a question with knowledge that may be located within a potentially large collection of documents. for conversational qa tasks like quac @xcite , the queries posed to the model take the form of a dialogue, where previous dialogue turns contain necessary context to answer the current turn's question. both of these task frameworks can be framed as either extractive qa or abstractive qa. document-grounded conversational question answering tasks like coqa @xcite and doc2dial @xcite combine the above two frameworks. this setting requires * equal contribution models to understand user queries and their associated dialogue context, use them to find relevant grounding documents, and then generate coherent responses to user queries. this pipe-lined architecture forms the backbone of the baseline model, henceforth called retriever-reader. in this paper, we present our approach to the multidoc2dial (mdd) task @xcite , the successor to doc2dial, which complicates the doc2dial setting by constructing dialogues that are grounded in multiple documents. each document is segmented into multiple passages, and thus document and passage are interchangeably used in this paper. as a result, models must retrieve the documents relevant to the current dialogue turn. these grounding documents could potentially be different from those grounded in previous dialogue turns. we propose a model that improves over the baseline model by focusing on each component of its retriever-reader architecture. firstly, we introduce sparse lexical representations in the retriever for matching, as outlined in @xcite . secondly, we rerank the retriever's results using techniques from @xcite . furthermore, we update the decoding process to incorporate the fusion-in-decoder (fid) technique @xcite . finally, we use curriculum learning to train our models. we observe an improvement of 11.9 (bleu) and 9.5 (f1) points on the validation; and an improvement of 9.5 (bleu) and 10.3 (f1) points on test set in the mdd-seen setting compared to the baseline rag model. we achieve 18.7 and 13.7 points improvement in the bleu and f1 metric respectively on the mdd-unseen test set compared to the baseline rag model. our submission (cmu-qa) stands 2 nd and 3 rd on the unseen and seen leaderboards foot_0 respectively. ## related works the multidoc2dial setting draws on related tasks like open-domain qa and conversational qa. consequently, we investigate techniques that have shown success on those tasks. conversational qa tasks, which typically assume that the grounding document is provided, use transformer-based architectures; the leading submissions to the quac and coqa leaderboards use roberta @xcite and deberta @xcite , respectively. retriever-reader architectures such as rag @xcite have become a popular choice for open-domain qa, increasingly using dense retrieval methods such as dpr @xcite . we study works related to four areas for modeling improvement: retrieval, reranking, reader, and training. retriever : as the multidoc2dial task is formulated in an open-domain setting, it requires the retrieval of relevant sources (passages) from a large pool of documents for generating the right output. hence, we investigate the strides in information retrieval in recent years. recently, dense retrieval based approaches have shown competitive performance @xcite @xcite while also scaling to large corpora, like ms-marco dataset @xcite . they use a nearest neighbor index, such as faiss @xcite to ensure scalability. dense retrieval techniques aims to encode the query and passage into a shared semantic space where the relevance of a passage for a query can be computed by the inner product of their representations. in contrast, sparse retrieval techniques perform exact token-level matching in the vocabulary space. there has been a growing interest in this field, with many advances achieving state-of-the-art results @xcite @xcite @xcite . these models are advantageous due to their interpretable representations, efficient lookup, highly scalable inverted-list indexing, and excellent performance in exact term-based matching scenarios. like dense retrieval based approaches, matches are computed via the dot product of the query and passage representations. reranking : while both dense and sparse retrieval methods have shown good progress, they must still embed the query and passage separately, because computing a match score between a query and ev-ery passage is computationally infeasible. as a compromise, re-ranking methods such as those in @xcite train a re-ranking module that can jointly embed the query and retrieved passages. because the set of retrieved passages is significantly smaller than the whole corpus, re-ranking methods can model more complex relationships between the query and retrieved passages, and significantly boost retrieval performance. reader : encoder-decoder based abstractive readers have been widely used in qa tasks. rag @xcite uses the bart-large model @xcite which is pre-trained using a denoising objective and a variety of different noising functions. moreover, rag marginalizes output from each (query, passage) pair based on retrieval scores. it has obtained state-of-the-art results on a diverse set of generation tasks and outperforms comparably-sized t5 models. fusion in decoder (fid) @xcite performs well in extractive-based qa tasks like natural questions @xcite . unlike rag model, the independent processing of the passages on the encoder side allows the fid model to scale to a large number of passages, while the fusion in the decoder effectively combines evidence from multiple passages. training : works such as @xcite have shown that fine-tuning a transformer model on examples, ordered on the basis of their difficulty, results in significant performance gains across different tasks. @xcite show more specifically that this type of curriculum design generalizes well to the document-grounded qa setting. ## task description multidoc2dial is a conversational qa task that requires generating responses to user queries. in contrast to tasks like its predecessor, doc2dial @xcite , and related tasks like quac @xcite , sharc @xcite , and coqa @xcite , which assume that the grounding document for the dialogue is given, mul-tidoc2dial constructs dialogues that are grounded in multiple documents. each dialogue is constructed from a number of segments. different segments are grounded in different documents; while all the dialogue turns within a segment are grounded in a single document. the dataset additionally marks the specific passage that is relevant to the current dialogue turn. however, the transitions between segments, which we refer to as topic shifts, are not marked. as a result, models are required not only to determine the grounding passages for each turn but also to determine which parts of the dialogue context continue to be relevant across topic shifts. the original dataset also presents several distinct domains of grounding documents from different public-facing websites that exhibit different writing styles. each dialogue is grounded in documents drawn from the same domain. the shared task defines two settings: one where all of the dialogues are grounded in documents from domains seen during training (mdd-seen), and another where the grounding domain is unseen (mdd-unseen). due to the open domain evidence retrieval and natural language response generation setting of the task, it lends itself well to a retriever-reader architecture. broadly speaking, the multidoc2dial task can be broken down into two distinct subtasks. models must first retrieve the correct grounding passage from the provided corpora. they must then use the retrieved passages to generate a response to the user query in the most recent dialogue turn. while the multidoc2dial paper defines both retrieval and a generation task, the shared task only evaluates reader outputs. models are evaluated on the sum of different metrics: f1, bleu (as implemented in post 2018), me-teor @xcite , and rougel @xcite . ## methodology for this task, we employ the standard retrieverreader architecture used in open-domain question answering. the model takes the user's current turn and dialogue context (previous turns) as the query. the query is then passed to the retriever which selects the top-n passages which are further passed to a reranker. the top-k (out of top-n) reranked passages are then fed to the reader along with the query to finally generate the agent's response. in our experiments, we use distilsplade (formal et al., 2021) as our retriever, which augments the query and passages, subsequently projecting them to a sparse vector in the vocabulary space. each coordinate in the projected vector represents the semantic importance of a term (also called "term impact" (mallia et al., 2021)) for matching. the inputs are augmented by applying a sparsityinducing activation function on the logits of a masked language model such as bert @xcite , which selects the important words present in the passage and adds additional expansion to combat the vocabulary mismatch problem. the sparsity of the activation is complemented with the flops regularizer @xcite the passages retrieved by the bi-encoder based retrieval are then passed through a roberta @xcite based cross-encoder. the roberta model is trained to output a score that denotes the relevance of a passage to the given query. due to the cross-attention between the query and the passage, the reranking proved to be effective by pulling up golden passages in the top-k documents that are passed on to the reader. an abstractive reader is used to generate agent responses. we use a t5 based fusion in decoder (fid) model which encodes all the top-k reranked passages one-by-one and concatenates them to form the input to the decoder. the decoder then learns to collect evidence from multiple passages to generate the response. we also experiment with training our model using a curriculum learning approach originally proposed by @xcite and then implemented on doc2dial by @xcite . to do so, we divide our training data randomly into 4 buckets, and train a teacher model on each bucket using fid-t5. we then calculate each teacher model's performance (bleu, rougel and meteor scores) on the other 3 buckets, which the teacher model has not seen during training. the training instances are then partitioned into "easy", "medium", and "hard" examples based on the scores chosen in @xcite . we train in four phases, and each phase is trained until convergence. in the first phase, we train on a third of the easy examples; in the second, on a disjoint third of the easy examples, and a third of the medium examples; in the third phase, a disjoint third of all of the three partitions, and in the final phase, we train on the entire training set. ## results & discussion ## conclusion we introduced our submission (cmu-qa) for the multidoc2dial shared task. our approach (r3) focuses on improving the overall retriever-reader pipeline using the sparse retriever distilsplade and fusion-in-decoder (fid) as the reader. we use a cross-attention based reranker to further boost recall scores. we refine the training process through curriculum learning to handle the diverse complexity of this dataset. for future work, we plan to improve results through better dialogue modelling and reducing noise or irrelevant information in the passages by taking top text spans. further, we will aim to select the best of all candidate responses using a response re-ranker.