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Title: Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering

Paper content:
Answering questions that require multi-hop reasoning at web-scale necessitates retrieving multiple evidence documents, one of which often has little lexical or semantic relationship to the question.
This paper introduces a new graph-based recurrent retrieval approach that learns to retrieve reasoning paths over the Wikipedia graph to answer multi-hop open-domain questions.
Our retriever model trains a recurrent neural network that learns to sequentially retrieve evidence paragraphs in the reasoning path by conditioning on the previously retrieved documents.
Our reader model ranks the reasoning paths and extracts the answer span included in the best reasoning path.
Experimental results show state-of-the-art results in three open-domain QA datasets, showcasing the effectiveness and robustness of our method.
Notably, our method achieves significant improvement in HotpotQA, outperforming the previous best model by more than 14 points.
Open-domain Question Answering (QA) is the task of answering a question given a large collection of text documents (e.g., Wikipedia).
Most state-of-the-art approaches for open-domain QA (Chen et al., 2017; Wang et al., 2018a; Lee et al., 2018; Yang et al., 2019) leverage non-parameterized models (e.g., TF-IDF or BM25) to retrieve a fixed set of documents, where an answer span is extracted by a neural reading comprehension model.
Despite the success of these pipeline methods in singlehop QA, whose questions can be answered based on a single paragraph, they often fail to retrieve the required evidence for answering multi-hop questions, e.g., the question in Figure 1 .
Multi-hop QA (Yang et al., 2018) usually requires finding more than one evidence document, one of which often consists of little lexical overlap or semantic relationship to the original question.
However, retrieving a fixed list of documents independently does not capture relationships between evidence documents through bridge entities that are required for multi-hop reasoning.
Recent open-domain QA methods learn end-to-end models to jointly retrieve and read documents (Seo et al., 2019; Lee et al., 2019) .
These methods, however, face challenges for entity-centric questions since compressing the necessary information into an embedding space does not capture lexical information in entities.
Cognitive Graph (Ding et al., 2019) incorporates entity links between documents for multi-hop QA to extend the list of retrieved documents.
This method, however, compiles a fixed list of documents independently and expects the reader to find the reasoning paths.
In this paper, we introduce a new recurrent graph-based retrieval method that learns to retrieve evidence documents as reasoning paths for answering complex questions.
Our method sequentially retrieves each evidence document, given the history of previously retrieved documents to form several reasoning paths in a graph of entities.
Our method then leverages an existing reading comprehension model to answer questions by ranking the retrieved reasoning paths.
The strong interplay between the retriever model and reader model enables our entire method to answer complex questions by exploring more accurate reasoning paths compared to other methods.
structure of the documents during the iterative retrieval process.
In addition, all of these multi-step retrieval methods do not accommodate arbitrary steps of reasoning and the termination condition is hard-coded.
In contrast, our method leverages the Wikipedia graph to retrieve documents that are lexically or semantically distant to questions, and is adaptive to any reasoning path lengths, which leads to significant improvement over the previous work in HotpotQA and SQuAD Open.
This paper introduces a new graph-based recurrent retrieval approach, which retrieves reasoning paths over the Wikipedia graph to answer multi-hop open-domain questions.
Our retriever model learns to sequentially retrieve evidence paragraphs to form the reasoning path.
Subsequently, our reader model re-ranks the reasoning paths, and it determines the final answer as the one extracted from the best reasoning path.
Our experimental results significantly advance the state of the art on HotpotQA by more than 14 points absolute gain on the full wiki setting.
Our approach also achieves the state-of-the-art performance on SQuAD Open and Natural Questions Open without any architectural changes, demonstrating the robustness of our method.
Our method provides insights into the underlying entity relationships, and the discrete reasoning paths are helpful in interpreting our framework's reasoning process.
Future work involves end-to-end training of our graph-based recurrent retriever and reader for improving upon our current two-stage training.
where W r ∈ R d×2d is a weight matrix, b r ∈ R d is a bias vector, and α ∈ R 1 is a scalar parameter (initialized with 1.0).
We set the global initial state a 1 to a parameterized vector s ∈ R d , and we also parameterize an [EOE] vector w [EOE] ∈ R d for the [EOE] symbol.
The use of w i for both the input and output layers is inspired by Inan et al. (2017); Press & Wolf (2017) .
In addition, we align the norm of w [EOE] with those of w i , by applying layer normalization (Ba et al., 2016) of the last layer in BERT because w [EOE] is used along with the BERT outputs.
Without the layer normalization, the L2-norms of w i and w [EOE] can be quite different, and the model can easily discriminate between them by the difference of the norms.

assistant: text: Graph-based recurrent retriever that learns to retrieve reasoning paths over Wikipedia Graph outperforms the most recent state of the art on HotpotQA by more than 14 points.