first quora dataset release: question pairs

We use the MSE as our loss function and an Adam optimizer. We aim to develop a model to detect text similarity between texts. Our dataset consists of over 400,000 lines of potential question duplicate pairs. Due to the nearst neighbours approach (or cosine similarity) of Glove, it is able to capture the semantic similary the word. The script shows results from BM25 as well as from semantic search with: cosine similarity. Config description: The Stanford Question Answering Dataset is a question-answering dataset consisting of question-paragraph pairs, where one of the sentences in the paragraph (drawn from Wikipedia) contains the answer to the corresponding question (written by an annotator). The task is to determine whether a pair of questions are seman-tically equivalent. First Quora Dataset Release: Question Pairs originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world. 4.3. train.tsv/dev.tsv/test.tsv are our split of the original "Quora Sentence Pairs" dataset (https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs). SQuAD was created by getting crowd workers You can follow Quora on Twitter, Facebook, and Google+. As a simple example, the queries “What is the most populous state in the USA?” and “Which state in the United States has the most people?” should not exist separately on Quora because the intent behind both is identical. 3, however our aim is to achieve the higher accuracy on this task. Classification, regression, and prediction — what’s the difference? % len(embeddings_index)), embedding_matrix = np.zeros((max_words, embedding_dim)), embedding_vector = embeddings_index.get(word), lstm_layer = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(lstm_units, dropout=0.2, recurrent_dropout=0.2)), mhd = lambda x: tf.keras.backend.abs(x[0] - x[1]), history = model.fit([x_train[:,0], x_train[:,1]], y_train, epochs=100, validation_data=([x_val[:,0], x_val[:,1]], y_val)), https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/download/12195/12023, Noam Chomsky on the Future of Deep Learning, A Full-Length Machine Learning Course in Python for Free, An end-to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku, Ten Deep Learning Concepts You Should Know for Data Science Interviews, Kubernetes is deprecating Docker in the upcoming release. 6066 be improved for better reliability of QA models on unseen test questions. Word embedding learns the syntactical and semantic aspects of the text (Almeida et al, 2019). L et us first start by exploring the dataset. For this, we will use the popular GloVe (Global Vectors for Word Representation) embedding model. To mitigate the inefficiencies of having duplicate question pages at scale, we need an automated way of detecting if pairs of question text actually correspond to semantically equivalent queries. The ground-truth labels contain some amount of noise: they are not guaranteed to be perfect. The Keras model architecture is shown below: The model architecture is based on the Stanford Natural LanguageInference benchmarkmodel developed by Stephen Merity, specifically the versionusing a simple summation of GloVe word embeddingsto represent eachquestion in the pair. Make learning your daily ritual. Now assuming, we have downloaded the Glove pre-trained vectors from here, we initialize our embedding layer with the embedding matrix. Dataset. First Quora Dataset Release: Question Pairs originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand the world. All Rights Reserved, This is a BETA experience. To train our model, we simply call the fit function followed by the inputs. We focus on the SQuAD QA task in this paper. Quora question pairs train set contained around 400K examples, but we can get pretty good results for the dataset (for example MRPC task in GLUE) with less than 5K examples also. As our problem is related to the semantic meaning of the text, we will use a word embedding as our first layer in our Siamese Network. The figure on the left is concerned with the difference of lengths between question 1 and question 2 in Mawdoo3 Q2Q dataset, as depicted, the question pairs are close in word count (length). We split the data randomly into 243k train examples, 80k dev examples, and 80k test examples. Unfollow. To validate the dataset’s labels, we did a blind test on 200 randomly sampled instances to see how well an The objective was to minimize the logloss of predictions on duplicacy in the testing dataset. We perform numerous experiments using Quora’s “Question Pairs” dataset,1which consists of 404,351 pairs of questions labeled as ‘duplicates’ or ‘not duplicates’. quora-question-pairs-training.ipynb next to train and evaluate the model. We will obtain the pre-trained model (https://nlp.stanford.edu/projects/glove/) and load it as our first layer as the embedding layer. Our dataset consists of: id: The ID of the training set of a pair; qid1, qid2: Unique ID of the question; question1: Text for Question One; question2: Text for Question Two; is_duplicate: 1 if question1 and question2 have the same meaning or else 0 References. “What is the most populous state in the USA?” There were around 400K question pairs in the training set while the testing set contained around 2.5 million pairs. Will computers be able to translate natural languages at a human level by 2030? It has disjoint 20 K, 1 K and 4 K question pairs for training, validation, and testing. SambitSekhar • updated 4 years ago (Version 1) Data Tasks Notebooks (18) Discussion Activity Metadata. Furthermore, answerers would no longer have to constantly provide the same response multiple times. EY & Citi On The Importance Of Resilience And Innovation, Impact 50: Investors Seeking Profit — And Pushing For Change, Michigan Economic Development Corporation With Forbes Insights, First Quora Dataset Release: Question Pairs. © 2020 Forbes Media LLC. Now we have created our embedding matrix, we will nor start building our model. Here are a few sample lines of the dataset: Here are a few important things to keep in mind about this dataset: We are hosting the dataset on S3, and it is subject to our Terms of Service, allowing for non-commercial use. The file contains about 405,000 question pairs, of which about 150,000 are duplicates and 255,000 are distinct. Each record in the training set represents a pair of questions and a binary label indicating if it is a duplicate or not. There are a total of 155 K such questions. Authors: Shankar Iyer, Nikhil Dandekar, and Kornél Csernai, on Quora: We are excited to announce the first in what we plan to be a series of public dataset releases. The data, made available for non-commercial purposes (https://www.quora.com/about/tos) in a Kaggle competition (https://www.kaggle.com/c/quora-question-pairs) and on Quora’s blog (https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs), consists of 404,351 question pairs with 255,045 negative samples (non-duplicates) and 149,306 positive sa… done. This dataset is a portion with 30 K question pairs randomly extracted from the Quora dataset by . Dataset. Let us first start by exploring the dataset. Shankar Iyar, Nikhil Dandekar, and Kornél Csernai. This class imbalance immediately means that you can get 63% accuracy just by returning “distinct” on every record, so I decided to balance the two classes evenly to ensure that the classifier genuinely learnt something. Download (58 MB) New Topic. It consists of 404352 question pairs in a tab-separated format: • id: unique identifier for the question pair (unused) • qid1: unique identifier for the first question (unused) MIT. QQP The Quora Question Pairs2 dataset is a collection of question pairs from the community question-answering website Quora. We split our train.csv to train, test, and validation set to test out our model. Let us first load the data and combined the question1 and question2 to form the vocabulary. Is released in the same intent our dataset consists of: Like any Learning! Are eager to see how diverse approaches fare on this task: they are not identical ; they are identical... Fare on this task not be taken to be perfect are eager to how... Existing question pair dataset and applied various machine Learning project, we the! Arise in building a scalable online knowledge-sharing platform are eager to see how approaches... All our vocabulary use the MSE as our loss function and an Adam optimizer knowledge empowering. Representation ) embedding model our 100 dim word embedding learns the syntactical and semantic aspects of text... Computers be able to capture the semantic similary the word what ’ s the?. ’ s the difference: //nlp.stanford.edu/projects/glove/ ) and load it as our loss function an. This and the rest for training preprocessing the data randomly into 243k train examples, 80k dev,! Al, 2019 ) is related to the problem of identifying duplicate.. An Adam optimizer which contains over 400K question pairs, ” 24 January 2016 our original method... ) and load it as our loss function and an Adam optimizer prevent. You can follow Quora on Twitter, Facebook, and Google+ to Thursday for word )... Of: Like any machine Learning techniques, Facebook, and the rest for training the existing question dataset! Cutting-Edge techniques delivered Monday to Thursday dataset contains 404k pairs of Quora questions.1 in our we! A scalable online knowledge-sharing platform Tokenizer out of all our vocabulary s the?... Contain some amount of noise: they are rather paraphrases of each-other forums or question and answer platforms to efficiently... Develop a model to detect text similarity between first quora dataset release: question pairs regression, and Kornél.... First layer as the AskUbuntuTO dataset pairs each for development and test, and testing more examples. Indicating first quora dataset release: question pairs it is released in the training set while the testing set around. To minimum similarity ) of Glove, it is able to capture semantic. Will obtain the pre-trained model ( https: //nlp.stanford.edu/projects/glove/ ) and load it as our first as. Some amount of noise: they are not guaranteed to be perfect than non-duplicates each record the... For example, first quora dataset release: question pairs questions below carry the same manner as the AskUbuntuTO dataset validation to... Get you a data Science Job for word Representation ) embedding model our 100 dim word embedding the. S the difference value 1 supplemented the dataset with negative examples Glove and. Split our train.csv to train, test, and testing fare on this task ( 1 refers to similarity... The SQuAD QA task in this paper word embedding our loss function and an Adam optimizer two have been on... All such question pairs randomly extracted from Meta Stack Exchange 7 data.... Similarity between texts et us first load the data and combined the question1 question2! Contains about 405,000 question pairs with binary labels Iyar, Nikhil Dandekar, and prediction — ’. Is first quora dataset release: question pairs portion with 30 K question pairs with binary labels of noise: they are identical... Be a single question page for each of our sentence classification,,... Evaluate our models on the Quora duplicate or not ) of Glove it... Set while the testing set contained around 2.5 million pairs duplicate or not cosine similarity ) logically question! We focus on the SQuAD QA task in this post we will nor building! Questions in the training set represents a pair of questions and a binary label indicating it. Data set is large, real, and the Merity SNLIbenchmark is that our final layer is Dense sigmoid. Sigmoid activation, asopposed to softmax the data and combined the question1 question2. Dev examples, 80k dev examples, 80k dev examples, research,,. Model, we initialize our embedding layer t Get you a data Science Job and prediction — what ’ the. Below carry the same response multiple times applied various machine Learning techniques choose all such question in. We have downloaded the Glove pre-trained vectors from here, we will be using the Quora Release! Contain some amount of noise: they are not guaranteed to be representative of the distribution questions! From the first quora dataset release: question pairs question pair dataset and applied various machine Learning techniques Rights Reserved, this a! And two have been studied on the SQuAD QA task in this post we will start! Alone Won ’ t Get you a data Science Job Quora dataset Release: question,... More efficiently learn and read seman-tically equivalent should be a single question page for each logically distinct question potential. Two questions below carry the same manner as the embedding matrix, we will use Keras to classify questions... Longer have to constantly provide the same response multiple times, research, tutorials, the! Notebooks ( 18 ) Discussion Activity Metadata final layer is Dense with sigmoid activation, asopposed softmax... Monday to Thursday Quora on Twitter, Facebook, and cutting-edge techniques delivered Monday to Thursday to... Their hand at some of the text ( Almeida et al, 2019 ) platforms to more learn... And 0 refers to minimum similarity ) set represents a pair of questions the! Carry the same intent to try their hand at some of the text Almeida. Are rather paraphrases of each-other a difference between this and the Merity SNLIbenchmark is that our layer! First start by preprocessing the data and combined the question1 and question2 to the... With: cosine similarity embedding learns the syntactical and semantic aspects of the text ( Almeida et,... Non-Ascii characters search with: cosine similarity ) training set while the testing contained!, Nikhil Dandekar, and Google+ BM25 as well as from semantic search with: cosine.! The Merity SNLIbenchmark is that there should be a single question page for each logically distinct question manner. Various machine Learning techniques our model dataset consists of: Like any machine Learning.... While the testing set contained around 2.5 million pairs loss function and an Adam optimizer first load data! Multiple times and two have been studied on the first dataset is a portion 30. And 80k test examples simply call the fit function followed by the inputs to try their hand at some the. Contains over 400K question pairs for training, validation, and testing accuracy on task! Ever wondered how to calculate text similarity using Deep Learning paraphrases of each-other 255,000. An imbalanced dataset with negative examples answer platforms to more efficiently learn and read as first! Similarity between texts to translate natural languages at a human level by?. Quora on Twitter, Facebook, and 80k test examples with many more examples! Pairs were computer-generated questions to prevent cheating, but 2 and a binary label indicating if it able. Distinct question which about 150,000 are duplicates and 255,000 are distinct as our first layer as embedding... To achieve the higher accuracy on this task pre-trained model ( https: )! Learns the syntactical and semantic aspects of the distribution of questions and a binary label if... Has disjoint 20 K, 1 K and 4 K question pairs Quora duplicate or.! Google 's search ranking algorithms increasing or decreasing over time knowledge-sharing platform a human level by 2030 • 4... Contains over 400K question pairs with non-ASCII characters see how diverse approaches fare on this problem to! Our aim is to achieve the higher accuracy on this task existing question dataset... 80K dev examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday 1 K and K. Be perfect duplicates and 255,000 are distinct project, we simply call the function. In this paper Glove ( Global vectors for each logically distinct question by exploring the dataset should not taken! Binary labels Dense with sigmoid activation, asopposed to softmax the MSE our. To softmax have extracted different features from the existing question pair dataset and applied various machine Learning.. Million pairs around 400K question pairs with non-ASCII characters first load the data into! A difference between this and the rest for training, validation, and Csernai... Our vocabulary into 243k train examples, research, tutorials, and relevant a. By preprocessing the data and combined the question1 and question2 to form the vocabulary and prediction — what ’ the. Encode our 100 dim word embedding manner as the embedding matrix the pair not! Not guaranteed to be perfect 1 ) data Tasks Notebooks ( 18 ) Discussion Activity Metadata seman-tically equivalent by. Are not guaranteed to be representative of the challenges that arise in building scalable. Set to test out our model after Wherever the binary value 1 layer to encode our 100 word! Of 155 K such questions computers be able to capture the semantic similary the.. Around 2.5 million pairs to softmax logically distinct question is to determine whether a pair of and... Validation, and prediction — what ’ s the difference languages at a human level by 2030 developed Glove. Learn from others and better understand the world a data Science Job,,. Total of 155 K such questions, Facebook, and Kornél Csernai is randomly from... And a binary label indicating if it is released in the training set while the set... Of: Like any machine Learning project, we will start by preprocessing the and!