A comparison of performance of several QA models on common QA datasets. Salient Spans Masking (proposed by REALM): Salient span masking is a special case for MLM task in language model training. 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. 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. This section covers R^3, ORQA, REALM and DPR. The focus of TREC QA track is to build a fully automatic open-domain question answering system, which can answer factual questions based on very large document. Recently [sic], Google has started incorporating some NLP (Natural Language Processing) in … Of course, the index would be much larger and the retrieval problem is more challenging. The pre-trained BERT model is fine-tuned on the training set of SQuAD, where all inputs to the reader are padded to 384 tokens with the learning rate 3e-5. The overview of R^3 (reinforced ranker-reader) architecture. Finding it difficult to learn programming? 15. Big language models have been pre-trained on a large collection of unsupervised textual corpus. Note: It is very important to standardize all the columns in your data for logistic regression. After recently watching the documentary The Social Dilemma, I'm really concerned about the privacy abuses of FAANG and other big tech companies, and I'm wondering how many NLP jobs are to be found outside of such companies.. It requires semi-complex pre-processing including tokenization and post-processing steps that are … [20] Hervé Jegou, et al. I have broken this problem into two parts for now -. The key difference of the BERTserini reader from the original BERT is: to allow comparison and aggregation of results from different segments, the final softmax layer over different answer spans is removed. In our previous post (Intro to Automated Question Answering), we covered the general design of these systems, which typically require two main components: the document retriever (a search engine) that selects the n most relevant documents from a large collection, and a document reader that processes these candidate documents in search of an explicit answer span. The original T5 models were pre-trained on a multi-task mixture including an unsupervised “masked language modeling” (MLM) tasks on the C4 (“Colossal Clean Crawled Corpus”) dataset as well as fine-tuned altogether with supervised translation, summarization, classification, and reading comprehension tasks. However, different from ICT in ORQA, REALM upgrades the unsupervised pre-training step with several new design decisions, leading towards better retrievals. All three components are learned based on different columns of the fine-tuned BERT representations. All the codes related to above concepts are provided here. Before we dive into the details of many models below. I will cache the text in my local environment because there is no need to download the same text again and again everytime I make changes to the system. Given a question \(x\) and a gold answer string \(y\), the reader loss contains two parts: (1) Find all correct text spans within top \(k\) evidence blocks and optimize for the marginal likelihood of a text span \(s\) that matches the true answer \(y\): where \(y=\text{TEXT}(s)\) indicates whether the answer \(y\) matches the text span \(s\). We only focus on a single-turn QA instead of a multi-turn conversation style QA. Only the question encoder needs to be fine-tuned for answer extraction. The retriever and reader models in the R^3 (“Reinforced Ranker-Reader”; Wang, et al., 2017) QA system are jointly trained via reinforcement learning. (2020) studied how the retrieved relevant context can help a generative language model produce better answers. The sentence having the answer is bolded in the context. (Image source: acl2020-openqa-tutorial/slides/part5). Overview of three frameworks discussed in this post. The target variable ranges from 0 to 9. It could be concerning, because there is a significant overlap between questions in the train and test sets in several public QA datasets. Ideas related to feature engineering or other improvements are highly welcomed. )\) is the glove word embedding. More demonstrations lead to better performance. cdQA. No explicit “black-box” IR system is involved. There are plenty of datasets and resources online, so you can quickly start training smart algorithms to learn and process massive quantities of human language data. 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: \(y = {\arg\max}_{k,i,j} F(x, z_k^{(i:j)})\). Dependency ParsingAnother feature that I have used for this problem is the “Dependency Parse Tree”. In other words, the evidence block encoder (i.e., \(\mathbf{W}_z\) and \(\text{BERT}_z\)) is fixed and thus all the evidence block encodings can be pre-computed with support for fast Maximum Inner Product Search (MIPS). However, considering the simple nature of the solution, this is still giving a good result without any training. 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. “Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets” arXiv:2008.02637 (2020). Once, the training data is created, I have used multinomial logistic regression, random forest & gradient boosting techniques. 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. 13. [13] Patrick Lewis et al. It can drastically speed up the inference time, because there is no need to re-encode documents for every new query, which is often required by a reader model. “Question Answering System”? attention  [6] Christopher Clark & Matt Gardner. (Image source: Guu et al., 2020). Note that the encoders for questions and context are independent. During training, ORQA does not need ground-truth context passages (i.e. RAG can be fine-tuned on any seq2seq task, whereby both the retriever and the sequence generator are jointly learned. We call this a Typed Dependency structure because the labels are drawn from a fixed inventory of grammatical relations. Find the related context in an external repository of knowledge; Extract the dense representations of a question \(x\) and a context passage \(z\) by feeding them into a language model; Use the dot-product of these two representations as the retrieval score to rank and select most relevant passages. I will give a brief overview, however, a detailed understanding of the problem can be found here. Create a vocabulary from the training data and use this vocabulary to train infersent model. Typical applications include intelligent voice interaction, online customer service, knowledge acquisition, personalized emotional chatting, and more. 2020). Why do you care about it? A model that can answer any question with regard to factual knowledge can lead to many useful and practical applications, such as working as a chatbot or an AI assistant. The dense vector \(d_k^{(i:j)}\) is effective for encoding local, The sparse vector \(s_k^{(i:j)}\) is superior at encoding precise. This is marginally improving the accuracy of the model by 5%. Check out this cool example in OpenAI API playground viewer. 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. 9. Bm25, a T5 with 11B parameters is able to match the of... 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