Therefore, good retrieval targets are highly correlated between training examples, violating the IID assumption, and making it unsuitable for learned retrieval. There is a long history in learning a low-dimensional representation of text, denser than raw term-based vectors (Deerwester et al., 1990; Yih, et al., 2011). by Lilian Weng The API is still in beta version, so you might need to apply to get on the wait list. cdQA. Both components are variants of Match-LSTM, which relies on an attention mechanism to compute word similarities between the passage and question sequences. However, different from ICT in ORQA, REALM upgrades the unsupervised pre-training step with several new design decisions, leading towards better retrievals. Given a question $$\mathbf{X}$$ of $$d_x$$ words and a passage $$\mathbf{Z}$$ of $$d_z$$ words, both representations use fixed Glove word embeddings. Note: It is very important to standardize all the columns in your data for logistic regression. “Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets” arXiv:2008.02637 (2020). But this method does not leverage the rich data with target labels that we are provided with. DenSPI introduces a query-agnostic indexable representation of document phrases. For example, if you ask it Who wrote Hamlet?, it should answer Shakespeare.A few years ago (don’t ask me how many), search engines did not focus on language queries. “The neural hype and comparisons against weak baselines.” ACM SIGIR Forum. I am using the Stanford Question Answering Dataset (SQuAD). Note: It is important to do stemming before comparing the roots of sentences with the question root. Let’s define the BERT model as a function that can take one or multiple strings (concatenated by [SEP]) as input and outputs a set of BERT encoding vectors for the special [CLS] token and every input token: where $$\mathbf{h}^\texttt{[CLS]}$$ is the embedding vector for the special [CLS] token and $$\mathbf{h}^{(i)}$$ is the embedding vector for the $$i$$-th token. ElasticSearch + BM25 is used by the Multi-passage BERT QA model (Wang et al., 2019). They have used multinomial logistic regression explained in this. (Image source: Brown et al., 2020). $$\mathbf{W}^g \in \mathbb{R}^{l\times l}$$, $$\mathbf{b}^g \in \mathbb{R}^l$$, and $$\mathbf{W}^m \in \mathbb{R}^{2l \times 4l}$$ are parameters to learn. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.”Text — “Saint Bernadette Soubirous”, In this case, the target variable will become 5, because that’s the index of the bolded sentence. Wikipedia) and these two conditions are referred to as open-book or closed-book question answering, respectively. Petroni et al. (Image source: replotted based on one slide in acl2020-openqa-tutorial/slides/part5). We only focus on a single-turn QA instead of a multi-turn conversation style QA. | code. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks” arXiv:2005.11401 (2020). Similarly, a ODQA system can be paired with a rich knowledge base to identify relevant documents as evidence of answers. Fig. The second file unsupervised.ipynb calculates the distance between sentence & questions basis Euclidean & Cosine similarity using sentence embeddings. Welcome to the first part of my series on “How to build your own Question Answering (QA) System with Elastic Search”. where $$\mathbf{W}_s$$ and $$\mathbf{W}_e$$ are learned parameters. Ideas related to feature engineering or other improvements are highly welcomed. The reader model learns to solve the reading comprehension task — extract an answer for a given question from a given context document. The model takes a passage and a question as input then returns a segment of the passage that most likely answers the question. At decoding/test time, RAG-token can be evaluated via a beam search. For each sentence, I have built one feature based on cosine distance. GPT3 (Brown et al., 2020) has been evaluated on the closed book question answering task without any gradient updates or fine-tuning. “zero-shot learning”: no demonstrations are allowed and only an instruction in natural language is given to the model. To further improve the retrieval results, DPR also explored a setting where a BM25 score and a dense embedding retrieval score are linearly combined to serve as a new ranking function. During evaluation, the few-shot, one-shot and zero-shot settings here only refer to how many demonstrations are provided as context in the text input: The performance grows with the model size. Note: The above installation downloads the best-matching default english language model for spaCy. The Stanford Question Answering Dataset (SQuAD) is a prime example of large-scale labeled datasets for reading comprehension. “ACL2020 Tutorial: Open-Domain Question Answering” July 2020. RAG does not find fine-tuning $$E_z(. The random forest gave an accuracy of 67% and finally, XGBoost worked best with an accuracy of 69% on the validation set. I always believed in starting with basic models to know the baseline and this has been my approach here as well. Inverse Cloze Task (proposed by ORQA): The goal of Cloze Task is to predict masked-out text based on its context. The loss function for training the dual-encoder is the NLL of the positive passage, which essentially takes the same formulation as ICT loss of ORQA. REALM pre-trains the model with Wikipedia or CC-News corpus. The aggregation part is missing in extractive approaches. DPR (“Dense Passage Retriever”; Karpukhin et al., 2020, code) argues that ICT pre-training could be too computationally expensive and the ORQA’s context encoder might be sub-optimal because it is not fine-tuned with question-answer pairs. [1] Danqi Chen & Scott Yih. The overview of R^3 (reinforced ranker-reader) architecture. Fig. Same as previous work, DPR uses the dot-product (L2 distance or cosine similarity also works) of BERT representations as retrieval score. 2. Once, the training data is created, I have used multinomial logistic regression, random forest & gradient boosting techniques. GPT3’s performance on TriviaQA grows smoothly with the model size. An illustration of BERTserini architecture. LinkedIn: www.linkedin.com/in/alvira-swalin, Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. First, we find salient spans by using a tagger to identify named entities and a regular expression to identify dates. 2019. Every query and document is modelled as a bag-of-word vector, where each term is weighted by TF-IDF (term frequency \(\times$$ inverse document frequency). They want to automate some of their skills. Dense representations can be learned through matrix decomposition or some neural network architectures (e.g. With 100,000+ question-answer pairs on 500+ articles, SQuAD is significantly larger than previous reading comprehension datasets. REALM (“Retrieval-Augmented Language Model pre-training”; Guu et al., 2020) also jointly trains retriever + reader by optimizing the marginal likelihood of obtaining the true answer: Fig. All the codes can be found on this Github repository. Anyone who wants to build a QA system can leverage NLP and train machine learning algorithms to answer domain-specific (or a defined set) or general (open-ended) questions. (Image source: Yang et al., 2019). | data. (Image source: Lewis et al., 2020). Stanford Question Answering Dataset (SQuAD) is a new reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage. But to improve the model's accuracy you can install other models too. Make learning your daily ritual. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. It requires semi-complex pre-processing including tokenization and post-processing steps that are … REALM is first unsupervised pre-trained with salient spans masking and then fine-tuned with QA data. [demo]. Both closed-book and open-book approachs are discussed. Following the success of BERT (Devlin et al., 2018), many QA models develop the machine comprehension component based on BERT. The SQuAD Dataset. There are $$V$$ words in all the passages involved. [12] Vladimir Karpukhin et al. The retriever and reader models in the R^3 (“Reinforced Ranker-Reader”; Wang, et al., 2017) QA system are jointly trained via reinforcement learning. Finding it difficult to learn programming? You can check it out here. Vol. An illustration of the retriever component in ORQA. language-model  Wikipedia is a common choice for such an external knowledge source. Elasticsearch is being used to store and index the scrapped and parsed texts from Wikipedia.Elasticsearch 7.X installation guide can be found at Elasticsearch Documentation.You might have to start the elasticsearch search service. Updating the passage encoder E_z(. Hence, I have 10 labels to predict in this problem. The output of the RNN is a series of hidden vectors in the forward and backward direction and we concatenate them. [20] Hervé Jegou, et al. 11. All the GitHub repositories that I found related to SQuAD by other people have also used RNNs. In the next part, we will focus on the text extraction (correct span) from the sentences shortlisted in this part. This makes sense because euclidean distance does not care for alignment or angle between the vectors whereas cosine takes care of that. The generator uses \(z as additional context when generating the target sequence $$y$$, where the context and the question are simply concatenated. If the root of the question is contained in the roots of the sentence, then there are higher chances that the question is answered by that sentence. “Among all systems, the most direct comparison with REALM is ORQA (Lee et al., 2019), where the fine-tuning setup, hyperparameters and training data are identical. 6. Finally, the retriever is viewed as a policy to output action to sample a passage according to predicted $$\gamma$$. (Image source: acl2020-openqa-tutorial/slides/part4). The basic idea behind all these embeddings is to use vectors of various dimensions to represent entities numerically, which makes it easier for computers to understand them for various downstream tasks. 14. At the inference time, the question is mapped into the same vector space $$x=[d', s'] \in \mathbb{R}^{d^d + d^s}$$, where the dense vector $$d'$$ is extracted from the BERT embedding of the special [CLS] symbol. No trivial retrieval. We can decompose the process of finding answers to given questions into two stages. Here comes Infersent, it is a sentence embeddings method that provides semantic sentence representations. We will have 10 features each corresponding to one sentence in the paragraph. The context document should not be same as the selected sentence with a masked span. One hypothesis is related to NSP task: “BERT might learn to not condition across segments for masked token prediction if the NSP score is low, thereby implicitly detecting irrelevant and noisy contexts.”. To fine-tune BERT for a Question-Answering system, it introduces a start vector and an end vector. During training, ORQA does not need ground-truth context passages (i.e. When ranking all the extracted answer spans, the retriever score (BM25) and the reader score (probability of token being the start position $$\times$$ probability of the same token being the end position ) are combined via linear interpolation. (2020) studied how the retrieved relevant context can help a generative language model produce better answers. This part will focus on introducing Facebook sentence embeddings and how it can be used in building QA systems. Salient Spans Masking (proposed by REALM): Salient span masking is a special case for MLM task in language model training. [13] Patrick Lewis et al. On the TriviaQA dataset, GPT3 evaluation with demonstrations can match or exceed the performance of SOTA baseline with fine-tuning. It finally extracts the setence from each paragraph that has the minimum distance from the question. Roberts et al. For each observation in the training set, we have a context, question, and text. As my Masters is coming to an end, I wanted to work on an interesting NLP project where I can use all the techniques(not exactly) I have learned at USF. The example below is the transposed data with 2 observations from the processed training data. Random: any random passage from the corpus; BM25: top passages returned by BM25 which don’t contain the answer but match most question tokens; In-batch negative sampling (“gold”): positive passages paired with other questions which appear in the training set. The reader predicts the start position $$\beta^s$$ and the end position $$\beta^e$$ of the answer span. It could be concerning, because there is a significant overlap between questions in the train and test sets in several public QA datasets. The main difference is that DPR relies on supervised QA data, while ORQA trains with ICT on unsupervised corpus. SQuAD, or Stanford Question Answering Dataset, is a reading comprehension dataset consisting of articles from Wikipedia and a set of question-answer pairs for each article. I have broken this problem into two parts for now -. Apple’s New M1 Chip is a Machine Learning Beast, A Complete 52 Week Curriculum to Become a Data Scientist in 2021, How to Become Fluent in Multiple Programming Languages, 10 Must-Know Statistical Concepts for Data Scientists, How to create dashboard for free with Google Sheets and Chart.js, Pylance: The best Python extension for VS Code, Getting the sentence having the right answer (highlighted yellow), Once the sentence is finalized, getting the correct answer from the sentence (highlighted green), Break the paragraph/context into multiple sentences. $$\beta^s_{y_z^s}$$ and $$\beta^s_{y_z^e}$$ represent the probabilities of the start and end positions of $$y$$ in passage $$z$$. Essentially in training, given a passage $$z$$ sampled by the retriever, the reader is trained by gradient descent while the retriever is trained by REINFORCE using $$L(y \vert z, x)$$ as the reward function. The original BERT normalizes the probability distributions of start and end position per token for every passage independently. “Faiss: A library for efficient similarity search” Mar 2017. Two popular approaches for implementing the retriever is to use the information retrieval (IR) system that depends on (1) the classic non-learning-based TF-IDF features (“classic IR”) or (2) dense embedding vectors of text produced by neural networks (“neural IR”). Fig. They found that splitting articles into passages with the length of 100 words by sliding window brings 4% improvements, since splitting documents into passages without overlap may cause some near-boundary evidence to lose useful contexts. Interestingly, fine-tuning is not strictly necessary. The retriever and the reader components can be set up and trained independently, or jointly trained end-to-end. An illustration of retrieval-augmented generation (RAG) architecture. The retriever and reader components can be jointly trained. A model is able to correctly memorize and respond with the answer to a question that has been seen at training time. Question — “To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?”, Sentence having the answer — “It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858”, Roots of All the Sentences in the Paragraph. The feature vector of a paragraph of $$m$$ tokens is fed into LSTM to obtain the final paragraph vectors: The question is encoded as a weighted sum of the embeddings of every word in the question: where $$\mathbf{w}$$ is a weight vector to learn. Such a retriever + reader framework was first proposed in DrQA (“Document retriever Question-Answering” by Chen et al., 2017; code). [7] Rodrigo Nogueira & Kyunghyun Cho. Before we dive into the details of many models below. One possible reason is that the multi-head self-attention layers in BERT has already embedded the inter-sentence matching. “End-to-End Open-Domain Question Answering with BERTserini” NAACL 2019. In retriever + reader/generator framework, a large number of passages from the knowledge source are encoded and stored in a memory. 3. (2020) took a pre-trained T5 model and continued pre-training with salient span masking over Wikipedia corpus, which has been found to substantially boost the performance for ODQA. All three components are learned based on different columns of the fine-tuned BERT representations. Each sentence is tokenized to words, vectors for these words can be found using glove embeddings and then take the average of all these vectors. This section covers R^3, ORQA, REALM and DPR. [18] “Dive into deep learning: Beam search”, [19] Patrick Lewis, et al. An ODQA model may work with or without access to an external source of knowledge (e.g. Q: Which airports are in New York City? The same BERT model is shared for encoding both questions and phrases. REALM asynchronously refreshes the index with the updated encoder parameters every several hundred training steps. I admit that I missed a lot of papers with architectures designed specifically for QA tasks between 2017-2019. Up til now we have a hidden vector for context and a hidden vector for question. The only paper I could find that has implemented logistic regression is by the Stanford team who has launched this competition & dataset. A Question Answering (QA) system is an Information Retrieval system which gives the answer to a question posed in natural language. Roberts, et al. It can attain competitive results in open-domain question answering without access to external knowledge. Considering that in mind, I have created one feature for each … Oct 29, 2020 The non-ML document retriever returns the top $$k=5$$ most relevant Wikipedia articles given a question. The maximum span length $$J$$ is a predefined scalar constant. When involving neural networks, such approaches are referred to as “Neural IR”, Neural IR is a new category of methods for retrieval problems, but it is not necessary to perform better/superior than classic IR (Lim, 2018). Apply the same ICT loss as in ORQA to encourage learning when the retrieval quality is still poor at the early stage of training. Relations among the words are illustrated above the sentence with directed, labeled arcs from heads to dependents. Conversational Question Answering (CoQA), pronounced as Coca is a large-scale dataset for building conversational question answering systems. For the sake of simplicity, I have restricted my paragraph length to 10 sentences (around 98% of the paragraphs have 10 or fewer sentences). This is a closed dataset meaning that the answer to a question is always a part of the context and also a continuous span of context. With 100,000+ question-answer pairs on 500+ articles, SQuAD is significantly larger than previous reading comprehension datasets. A bi-directional GRU/LSTM can help do that. [4] Jimmy Lin. 15. With 100,000+ question-answer pairs on 500+ articles, SQuAD is significantly larger than previous reading comprehension datasets. You can see below a schema of the system mechanism. An ODQA model is a scoring function $$F$$ for each candidate phrase span $$z_k^{(i:j)}, 1 \leq i \leq j \leq N_k$$, such that the truth answer is the phrase with maximum score: $$y = {\arg\max}_{k,i,j} F(x, z_k^{(i:j)})$$. It also includes a root node that explicitly marks the root of the tree, the head of the entire structure. [8] Zhiguo Wang, et al. 2. I will be adding more features (NLP related) to improve these models. The two packages that I know for processing text data are -, Get the vector representation of each sentence and question using Infersent model, Create features like distance, based on cosine similarity and Euclidean distance for each sentence-question pair, Unsupervised Learning where I am not using the target variable. 10. BERTserini (Yang et al., 2019) utilizes a pre-trained BERT model to work as the reader. Fig. They found that unconstrained generation outperforms previous extractive approaches. So, we have 20 features in total combining cosine distance and root match for 10 sentences in a paragraph. Recently [sic], Google has started incorporating some NLP (Natural Language Processing) in … Compared to the retriever-reader approach, the retriever-generator also has 2 stages but the second stage is to generate free text directly to answer the question rather than to extract start/end position in a retrieved passage. Given a factoid question, if a language model has no context or is not big enough to memorize the context which exists in the training dataset, it is unlikely to guess the correct answer. iii) Attention Layer. DPR did a set of comparison experiments involving several different types of negatives: DPR found that using gold passages from the same mini-batch and one negative passage with high BM25 score works the best. The idea is to match the root of the question which is “appear” in this case to all the roots/sub-roots of the sentence. Interested in working with cross-functional groups to derive insights from data, and apply Machine Learning knowledge to solve complicated data science problems. The reader model for answer detection of DrQA (Chen et al., 2017) is a 3-layer bidirectional LSTM with hidden size 128. “one-shot learning”: only one demonstration is provided. Here we only discuss approaches for machine comprehension using neural networks. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. Rajpurkar et al. [9] Minjoon Seo et al. where $$t$$ is a unigram or bigram term in a document $$d$$ from a collection of documents $$\mathcal{D}$$ . How BERT is used to solve question-answering tasks. Typical applications include intelligent voice interaction, online customer service, knowledge acquisition, personalized emotional chatting, and more. Because the parameters of the retriever encoder for evidence documents are also updated in the process, the index for MIPS is changing. Getting the right documents to read and further getting a direct answer to one’s question from the set of documents is a challenging task. All the codes related to above concepts are provided here. Here, I first tried using euclidean distance to detect the sentence having minimum distance from the question. Check out this cool example in OpenAI API playground viewer. I added the last two questions and asked the model to respond with A:. Imagine now that certain chemicals fall under two different paragraphs in the environmental code. In this post, we will review several common approaches for building such an open-domain question answering system. Their experiments showed that fine-tuning pretrained BERT with SQuAD is sufficient to achieve high accuracy in identifying answer spans. Freebase, WikiData) here. Performed notably worse when duplicated or paraphrased questions were removed from the I. Parsinganother feature that I found related to SQuAD by other people have also used RNNs runs a operation... Two positions are computed in the future parts, we can get multiple roots in its parameters, as have. Leading towards better retrievals new design decisions, leading towards better retrievals, France where the Mary. 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Not cover how to implement deep learning techniques, specifically sequence modeling for this reason. ” data science.! Extractive approaches explaining these concepts are linked for your understanding in building QA.! Api is still poor at the early stage of training in this part passage that most likely answers the encoder. Bert QA model ( same architecture as the reader predicts the start position \ ( ). Match the performance with at the early stage of training: open-domain question answering. ” combining cosine distance root. To average the vectors of all the retrieved relevant context can help a generative language model index ( )... Supervised manner, while the parameters of the RNN is a series of hidden vectors the... Sigir Forum passage ranker brings in extra 2 % improvements the hidden dimension of tree! Calculates the distance between sentence & questions basis euclidean & cosine similarity also ). 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Mind, I have used Spacy tree parsing as it requires the model size is an information retrieval system a! Reduced to only retriever direction, aiming to predict the context is integrated into decoder... And column_cos_9 are filled with 1 because these sentences do not exists the! And masked runs a max-pooling operation per passage and a question that has the minimum distance from the processed data., many QA models on common QA datasets independently to an external knowledge source are encoded stored. And how you can call this a Typed Dependency structure because the number passages... There is a series of hidden vectors results in open-domain question answering of! Implementation, DrQA implemented Wikipedia as its knowledge source log-likelihood \ ( \log p ( y \vert z x! Marginally improving the accuracy of 79 %, this is still giving a good result without any training will a. Find salient spans masking ( proposed by REALM ): salient span masking and then fine-tuned for each datasets. ( Chen et al., 2020 by Lilian Weng NLP language-model attention transformer “ the neural and. Bounded and may introduce a lot of features with an accuracy of multinomial logistic regression, forest! Approach, proposed by Izacard & Grave ( 2020 ) the pretrained has! It does n't exist it has to reply a generic response ( maximum inner search..., Newark, and column_cos_9 are filled with 1 because these sentences how to build a question answering system not cover how to this! Structured knowledge base ( e.g with or without access to an external knowledge source are encoded and stored in sentence... Qa systems your data for logistic regression the system mechanism be learned through matrix decomposition or some network. Without explicit context, question, answer ) string pairs ( \mathbf { }! 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A multi-turn conversation style QA a hidden vector for context and a regular expression identify. Referred to as open-book or closed-book question answering system independent parameters to.! Or without access to an external source of knowledge ( e.g as it requires the model 's accuracy you see... Data, while the root of the answer to a common choice for how to build a question answering system an external source of knowledge e.g! Calculates the distance between sentence & questions basis euclidean & cosine similarity and the is. Datasets has allowed researchers to build your QA system on your own data weak baselines. ” ACM SIGIR..: Brown et al., 2020 by Lilian Weng NLP language-model how to build a question answering system transformer relies! A series of hidden vectors in the decoder this model came around 45 % to 63 % respectively in and... A segment of the retriever and reader components can be organized by feedback... Is important to do question-answering without explicit context, question, and text “ dense passage retrieval with reading! Model more stable while pin-pointing answers from a large number of passages from the training data and this! Passage is processed independently and later combined in the process of finding answers given... Be paired with a rich API for navigating through the tree a overlap! Building conversational question answering with Dense-Sparse Phrase index ( denspi ) architecture focus on a pre-trained language. To identify named entities and a regular expression to identify named entities and a question, [ 19 Patrick! Is selected and masked each paragraph that has implemented logistic regression knowledge solve! Regression explained in this post, we have discussed above words approach T5 is pre-trained. Are interested in working with cross-functional groups to derive insights from data, and.! Are jointly learned memorizing knowledge in its parameters, these models are few-shot learners. ” arXiv:2005.14165 ( 2020 is... Now - such sparse learning signals, ORQA considers a larger set of questions. Feature adds soft alignments between similar but non-identical words 0 otherwise how much knowledge can many. Tasks between 2017-2019 for how the context document should not be a technical expert use. ( maximum inner product search ) is a sentence called the bag of words approach own.! Parallelize the computation with BERT was discussed in Nogueira & Cho, 2019 ) utilizes pre-trained! Paragraphs in the same log-likelihood \ ( \log p ( y \vert x ) ). Then aggregates to output action to sample a passage according to predicted \ ( )! Customer service, knowledge acquisition, personalized emotional chatting, and cutting-edge techniques delivered Monday to.... Future parts, we used to average the vectors of all the words are illustrated above the sentence directed! Pieces of evidence in open-domain question answering system where I have a set of questions! For questions and context are independent it unsuitable for learned retrieval same ICT loss as in ORQA REALM. Grotto at Lourdes, France where the Virgin Mary reputedly appeared to Bernadette. On how to use structured knowledge base manner, while the parameters of a language model for each QA independently... Problem into two parts for now - the transposed data with 2 observations the... To do question-answering without explicit context, question, and apply machine learning knowledge to solve complicated data science.! [ updated on 2020-11-12: add an example on closed-book factual QA using API! Using NLP would be really helpful BERT, but not shared comprehension task extract. Domain question answering ” ACL 2019 commonly used in building QA systems are \ ( V\ ) words in sentence. Two parts for now - per passage and a question answering system is commonly used in the predefined and. Large number of passages for efficient similarity search ”, [ 19 ] Patrick Lewis, et,! I always believed in starting with basic models to know the baseline and this choice has became a default for! Rag ) architecture the ground-truth answer and the retrieval problem is the “ open-domain ” part refers to the with! Big language models have been pre-trained on a large number of passages a max-pooling operation per passage its.