Definition: In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Positive Example 1 -
Input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
Output: No

Positive Example 2 -
Input: Since INLINEFORM0 is constant, we only need to minimize INLINEFORM1 , therefore the loss function becomes: DISPLAYFORM0 
 Question: How is the expectation regularization loss defined?
Output: DISPLAYFORM0

Negative Example 1 -
Input: The minimal change operation substantially changed the meaning of the sentence, and yet the embedding of the transformation lies very closely to the original sentence (average similarity of 0.930). 
 Question: Do they do any analysis of of how the modifications changed the starting set of sentences?
Output: No

Negative Example 2 -
Input: Looking at the current WinoGrande leaderboard, it appears that the previous state of the art is based on RoBERTa BIBREF2, which can be characterized as an encoder-only transformer architecture. 
 Question: What is the previous state of the art?
Output: GPT-3

Now complete the following example -
Input: Moreover, QA and QG have probabilistic correlation as both tasks relate to the joint probability between $q$ and $a$ . Given a question-answer pair $\langle q, a \rangle $ , the joint probability $P(q, a)$ can be computed in two equivalent ways.

$$P(q, a) = P(a) P(q|a) = P(q)P(a|q)$$ (Eq. 1)

The conditional distribution $P(q|a)$ is exactly the QG model, and the conditional distribution $P(a|q)$ is closely related to the QA model. Existing studies typically learn the QA model and the QG model separately by minimizing their own loss functions, while ignoring the probabilistic correlation between them.

Based on these considerations, we introduce a training framework that exploits the duality of QA and QG to improve both tasks. There might be different ways of exploiting the duality of QA and QG. In this work, we leverage the probabilistic correlation between QA and QG as the regularization term to influence the training process of both tasks. Specifically, the training objective of our framework is to jointly learn the QA model parameterized by $\theta _{qa}$ and the QG model parameterized by $\theta _{qg}$ by minimizing their loss functions subject to the following constraint.

$$P_a(a) P(q|a;\theta _{qg}) = P_q(q)P(a|q;\theta _{qa})$$ (Eq. 3)

$P_a(a)$ and $P_q(q)$ are the language models for answer sentences and question sentences, respectively. Overall, the framework includes three components, namely a QA model, a QG model and a regularization term that reflects the duality of QA and QG. The QA specific objective aims to minimize the loss function $l_{qa}(f_{qa}(a,q;\theta _{qa}), label)$ , where $label$ is 0 or 1 that indicates whether $a$ is the correct answer of $q$ or not. For each correct question-answer pair, the QG specific objective is to minimize the following loss function,

$$l_{qg}(q, a) = -log P_{qg}(q|a;\theta _{qg})$$ (Eq. 6)

where $a$ is the correct answer of $q$ . The third objective is the regularization term which satisfies the probabilistic duality constrains as given in Equation 3 . Specifically, given a correct $\langle q, a \rangle $ pair, we would like to minimize the following loss function,

$$ \nonumber l_{dual}(a,q;\theta _{qa}, \theta _{qg}) &= [logP_a(a) + log P(q|a;\theta _{qg}) \\ & - logP_q(q) - logP(a|q;\theta _{qa})]^2$$ (Eq. 9)

where $P_a(a)$ and $P_q(q)$ are marginal distributions, which could be easily obtained through language model. 
 Question: What does "explicitly leverages their probabilistic correlation to guide the training process of both models" mean?
Output: 



Answer: The framework jointly learns parametrized QA and QG models subject to the constraint in equation 2. In more detail, they minimize QA and QG loss functions, with a third dual loss for regularization.