# Neural Machine Reading Comprehension: Methods and Trends
## 5 New Trends
### Multi-Passage Machine Reading Comprehension  
In MRC tasks, the relevant passages are pre-identified, which contradicts the question-answering process of humans. People usually ask a question first and then search for possibly related passages, where they find evidence to give the answer. To overcome this shortcoming, [PERSON] et al. [45] extend MRC to machine reading at scale, more widely called multi-passage machine reading comprehension, which does not give one relevant passage for each question, unlike the traditional task. This extension can be applied to tackle open-domain question-answering tasks based on a large corpus of unstructured text. With its appearance, some multi-passage MRC task-specific datasets have been released, such as MS MARCO [7], TriviaQA [73], SearchQA [75], DuReader [77], and QUASAR [91]. In contrast to MRC, in a multi-passage MRC setting, the given context \(C\) is not a single passage, but a collection of documents \(\mathbb{D}\). The definition of multi-passage MRC tasks changes as shown in Table 13:  
Compared to MRC tasks, multi-passage MRC is far more challenging. For instance, although the DrQA model [45] achieves exact match accuracy of 69.5 on SQuAD, when applied to an open-domain setting (using the whole Wikipedia corpus to answer the question), its performance drops dramatically. The unique features of multi-passage MRC listed below are the main reasons for the degradation:  
* Massive Document Corpus  
This is the most prominent feature of multi-passage MRC, which makes it distinct from MRC, with one related passage. Under this circumstance, whether a model can retrieve the most relevant documents from the corpus quickly and correctly decides the final performance of reading comprehension. * Noisy Document Retrieval \begin{table}
\begin{tabular}{l} \hline \hline
**Multi-Passage Machine Reading Comprehension** \\ \hline Given a collection of \(m\) documents \(\mathbb{D}=\{D_{1},D_{2},\cdots,D_{m}\}\) and the question \(Q\), the multi-passage MRC task \\ requires giving the correct answer \(A\) to question \(Q\) according to documents \(\mathbb{D}\) by learning the function \(\mathcal{F}\) \\ such that \(A=\mathcal{F}(\mathbb{D},Q)\). \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Definition of multi-passage machine reading comprehension. Multi-passage MRC can be regarded as a distantly supervised open-domain question-answering task that may suffer from noise issues. Sometimes the model may retrieve a noisy document that contains the correct answer span, but it is not related to the question. This noise will mislead the understanding of the context. When the retrieval component does not perform well, there will be no answers in the document. If the answer extraction module ignores that, it outputs an answer even if it is incorrect, which will lead to performance degradation. In the open-domain setting, it is common to find multiple answers for a single question. For example, when asking _Who is the president of the United States_, both _[PERSON]_ and _[PERSON]_ are possible answers, but which is the correct answer requires reasoning based on the context. Evidence Aggregation  
In terms of some complicated questions, snippets of evidence can appear in different parts of one document or even in different documents. To answer such questions correctly, a multi-passage MRC model needs to aggregate all the evidence. More documents mean more information, which contributes to more complete answers. To address multi-passage MRC problems, one method follows the pipeline of \"retrieve then read.\" More specifically, the retrieval component first returns several relevant documents, which are then proposed by the reader to give the answer. DrQA, introduced by [PERSON] et al. [45], is a typical pipeline-based multi-passage MRC model. In the retrieval component, they use TF-IDF to select five relevant Wikipedia articles for each question in SQuAD to narrow the search space.