You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Existing PLMs based re-rankers typically improve ranking performance from two aspects: (1) By optimizing the ranking procedure: monoBERT (Nogueira and Cho, 2019) is the first work that re-purposed BERT as a passage re-ranker and achieves state-of-the-art results. duoBERT (Nogueiraet al., 2019a) integrates monoBERT in a multistage ranking architecture and adopts a pairwise classification approach to passage relevance computation. UED (Yanet al., 2021) proposes a cascade pre-training manner that can jointly enhance the retrieval stage through passage expansion with a pre-trained query generator and thus elevate the re-ranking stage with a pre-trained transformer encoder. The two stages can facilitate each other in a unified pre-training framework. H-ERNIE (Chuet al., 2022) proposes a multi-granularity PLM for web search.(2) By designing rational distillation procedure: LM Distill + Fine-Tuning (Gaoet al., 2020) explores a variety of distillation methods to equip a smaller re-ranker with both general-purpose language modeling knowledge learned in pre-training and search- specific relevance modeling knowledge learned in fine-tuning, and produces a faster re-ranker with better ranking performance. CAKD (Hofstätter et al., 2020) proposes a cross-architecture knowledge distillation procedure with a Margin-MSE loss, which can distill knowledge from multiple teachers at the same time. RocketQAv1 (Qu et al., 2021) trains dual-encoder and cross-encoder in a cascade manner, which leverages the powerful cross-encoder to empower the dual-encoder. RocketQAv2 (Ren et al., 2021) proposes a novel approach that jointly trains the dense passage retriever and passage re-ranker. The parameters of RocketQAv2 are inherited from RocketQAv1. Besides, RocketQAv2 utilizes a large PLM for data augmentation and denoising, which can also be regarded as a distillation procedure. Notably, these two types of studies anticipate more insightful information to be captured by the advanced ranking and training procedures, while neglecting the limitations of implicit knowledge extracted from noisy and heterogeneous data. Therefore, in this paper, we proposed the first knowledge-enhanced PLM based re-ranker, which thoughtfully leverages explicit external knowledge that improve the effectiveness of the model. We include several PLMs based re-rankers in our evaluation, including the state-of-the-art:•monoBERT (Nogueira and Cho, 2019): The first study that re-purposes BERT as a re-ranker and achieves state-of-the-art results.•duoBERT (Nogueiraet al., 2019a):This work proposes a pairwise classification approach using BERT, which obtains the ability to be more sensitive to semantics through greater computation.•UED (Yanet al., 2021): A unified pre-training framework that jointly refines re-ranker and query generator. For a fair comparison, we only use the re-ranker in UED without passage expansion.•LM Distill+Fine-Tuning (LDFT) (Gaoet al., 2020):A variety of distillation methods are compared in this paper. The experimental results indicate that a proper distillation procedure (i.e. first distill the language model, and then fine-tune on the ranking task) could produce a faster re-ranker with better ranking performance.•CAKD (Hofstätter et al., 2020): This work proposes a cross-architecture knowledge distillation procedure with Margin-MSE loss, which can distill knowledge from multiple teachers.•RocketQAv1 (Qu et al., 2021): This work mainly focuses on the training of PLM based retriever, where the re-ranker is an intermediate product of its training process.•RocketQAv2 (Ren et al., 2021): Based on RocketQAv1, this work proposes a novel approach that jointly trains the PLM based retriever and re-ranker.To compare the performance of different methods, we resort to two ranking metrics.For MSMARCO-DEV, We adopt Mean Reciprocal Rank (i.e., MRR@10).For TREC 2019 DL, we use Mean Average Precision, i.e., MAP@10 and MAP@30.For Ohsumed, both Mean Reciprocal Rank and Mean Average Precision (i.e., MRR@10 and MAP@10) are employed for comprehensive performance analysis in queries requiring in-depth domain knowledge.

Question: What is the difference of RocketQAv1 and RocketQAv2 model?
RocketQAv1 trains dual-encoder and cross-encoder in a cascade manner, which leverages the powerful cross-encoder to empower the dual-encoder. While it inherits the parameters from RocketQAv1, RocketQAv2 extends the first version through a novel approach that jointly trains the dense passage retriever and passage re-ranker, and by using a large PLM for data augmentation and denoising (i.e.,  a distillation procedure).