# Neural Machine Reading Comprehension: Methods and Trends
## 5 New Trends
### Conversational Machine Reading Comprehension  
MRC requires answering a question based on the understanding of a given passage, with questions usually isolated from each other. However, the most natural way that people acquire knowledge is via a series of interrelated question-and-answer processes. When given a document, someone will ask a question, and someone else gives an answer. Then based on the answer, a related question is asked for deeper understanding. This process is performed iteratively, which can be regarded as a multi-turn conversation. After incorporating conversation into MRC, conversational machine reading comprehension (CMRC) has become the research hot spot. In contrast to MRC, conversation history \(H\) also acts as part of the context to help with answer prediction in CMRC. The definition of CMRC can be formulated as shown in Table 14:Many researchers have tried to create new datasets with given passages and a series of conversations to satisfy the requirements of CMRC tasks. [PERSON] et al. [99] release CoQA, a CMRC dataset with 8000 conversations about passages in seven domains. In CoQA, a questioner asks questions based on a given passage and an answerer gives answers, simulating a conversation between two humans when reading the passage. There is no limit to the answer forms in CoQA, which requires more contextual reasoning. Similarly, [PERSON] et al. [100] introduce QuAC for question answering in context. Compared to CoQA, in QuAC, the passage is given only to the answerer, and the questioner asks the questions based on the title of the passages. The answerer answers the questions with subsequences of the original passage and determines whether the questioner can ask a follow-up question. [PERSON] et al. [101] extend cloze test tasks to the conversational setting. They use dialogues between characters selected from transcripts of the TV show _Friends_ to generate related context and ask to fill in the blanks with character names according to utterances and context. In contrast to the above two datasets, it is aimed at multi-party dialogue and pays attention to the doers of some actions. To illustrate the CMRC task more specifically, some examples in CoQA datasets are presented in Table 15.  
CMRC brings about some new challenges compared to MRC:  
* Conversational History  
In MRC tasks, questions and answers are based on given passages and questions are independent from the previous question-answering process. In contrast to that, conversational history plays an important role in CMRC. A follow-up question may be closely related to prior questions and answers. More specifically, as shown in Table 15, question 4 and 5 are relevant to question 3. Moreover, answer 3 can be a verification for answer 5. To face this challenge, dialogue pairs as conversational history are also fed to the CMRC systems as inputs. * Coreference Resolution \begin{table}
\begin{tabular}{l l} \hline \hline
**CMRC** & \\ \hline Passage: & [PERSON] went to sit in her rocking chair. Today was her birthday and she was turning 80. Her \\  & granddaughter [PERSON] was coming over in the afternoon and [PERSON] was very excited to see \\  & her. Her daughter [PERSON] and [PERSON]’s husband [PERSON] were coming as well. \\ \hline Question 1: & Who had a birthday? \\ Answer 1 : & [PERSON] \\ \hline Question 2: & How old would she be? \\ Answer 2 : & 80 \\ \hline Question 3: & Did she plan to have any visitors? \\ Answer 3 : & Yes \\ \hline Question 4: & How many? \\ Answer 4 : & Three \\ \hline Question 5: & Who? \\ Answer 5: & [PERSON], [PERSON] and [PERSON] \\ \hline \hline \end{tabular}
\end{table} 
Table 15: An example of the CoQA datasetCoreference resolution is a traditional task in natural language processing, and it is even more challenging in CMRC. The coreference phenomenon not only may occur in the context, but may appear in the question-and-answer sentences as well. Coreference can be sorted into two kinds, explicit and implicit. With explicit coreference, there are explicit markers, such as some personal pronouns.