Bruno Goutorbe
Bruno Goutorbe Chief Data Scientist at Cdiscount
6 min read

Our participation to the Kaggle challenge: Quora Question Pairs

Our participation to the Kaggle challenge: Quora Question Pairs

So, we decided to spend a little of our time on a Kaggle challenge, namely, Quora Question Pairs. (By “we”, I mean the data scientists of Cdiscount.) The purpose of the challenge consisted in detecting duplicate questions, that is, pairs of questions carrying the same meaning. This is of interest to us, as we work on loosely related issues: for exemple, we need to detect algorithmically duplicate products in our catalog(which contains more than 20 million active items) or to match themagainst competitors’ catalogs.

The challenge took place from March 16th to June 6th, 2017. We came around quite late, about one month after it started, so we mostly ran after the score andfinished in the top 8%. Here:

The challenge

As its name indicates, the challenge was organized by the question-and-answer website, Quora. The objective consisted in submitting the best machine learning modelcapable of predicting whether two questions are duplicates of each other,or not. In order to train the models, Quora released a set of more than400,000 pairs or questions with a label: duplicate or not duplicate.

Training set

See some examples of questions that are not duplicates (about 60% of the set):

question 1 question 2 is_duplicate
What is the step by step guide to invest in share market in india? What is the step by step guide to invest in share market? 0
How can I increase the speed of my internet connection while using a VPN? How can Internet speed be increased by hacking through DNS? 0
Which one dissolve in water quikly sugar, salt, methane and carbon dioxide? Which fish would survive in salt water? 0

And some examples of duplicates (about 40% of the set):

question 1 question 2 is_duplicate
How can I be a good geologist? What should I do to be a great geologist? 1
How do I read and find my YouTube comments? How can I see all my Youtube comments? 1
What can make Physics easy to learn? How can you make physics easy to learn? 1

Testing set

As for the testing set, it contains more than 2,3 millions pairs of questions. Have a look:

question 1 question 2
How does the Surface Pro himself 4 compare with iPad Pro? Why did Microsoft choose core m3 and not core i3 home Surface Pro 4?
What but is the best way to send money from China to the US? What you send money to China?
How “aberystwyth” start reading? How their can I start reading?

Uh? Many questions don’t seem to make sense at all, but we’ll come back to it later.

Evaluation metric

For each pair of the testing set, the submitted model is expected to predict a probability of the questions to be duplicates. Quora chose the logistic loss metric to evaluate the quality of the predictions. In short, this metric strongly penalizes models that are confident about an incorrect classification.


Feature enginering

Feature construction was the core ingredient of the challenge. The participants (including ourselves) built a wide variety of input variables, which generally included:

  • Simple statistics, such as the length (and difference in lengths) of the pairs of questions;
  • Similarities between the pairs based on the characters constituting the questions,such as Levenshtein and related similarities used in fuzzy string matching;
  • Similarities between the pairs based on the set of words constituting the questions, such as the Jaccard index;
  • Various distances (L1, L2, cosine, canberra…) between vector representationsof the questions derived from TF-IDF statistics and pre-trained Word2Vec models (Google News, Glove, FastText);
  • Presence of important words such a why, when… in the questions.

This is obviously a time-consuming task which was performed incrementally. By the end of the challenge we came up with about a hundred features. This is somewhat far from the best-ranked submissions, which, according to what we read in the discussion forum, reached several hundreds of features.

Interestingly, some of our data scientists adopted a more “brute-force” approach which consisted in defining one feature per word of the corpus taking on the value of

  • 0: word present in none of the questions,
  • 1: word present in only one question,
  • or 2: word present in both questions.

In other words the features corresponded to a sum of binary TF applied to the questions. This approach had the advantage of moving part of the effort of identifying discriminating words or combinations of words to the (training phase of the) models. The inconvenient being that it led to over-bloated models, e.g., xgboost models with tens of thousands of trees taking the whole night to train.

Leaky features

During the feature engineering phase, participants soon identified leaky features, that is, highly predictive variables related to biases in the way the testing set was built rather than real relationships with respect to the property to predict, thus useless in a real-world application. Two such features surfaced in the discussion forum:

  • the total number of occurences of each question, within both the training and testing sets;

  • the number of common neighbours between each pair of questions: neighbours are defined as questions appearing together in a pair, either in the training set or in the testing set, be they labelled as duplicates or not.

These two leaking features allow decreasing the score by about 0.1: this is a huge improvement, if you look at the top figure showing the final rank vs. score. We suspect that the best ranked submissions found and exploited more leaks in the data sets. These leaks could be related to the way questions were selected in the sets (e.g., questions having at least one identified duplicate may have been oversampled); or to the computer-generated questions included in the testing set as anti-cheating measure (remember, those silly questions we spotted above?).

In any case, these leaks should remind data science teams willing to organize Kaggle-like challenges that great caution should be exercised in preparing the data sets.


This challenge confirmed the Extreme Gradient Boosting model (xgboost) as an indispensable method for anyone willing to obtain a decent score within a Kaggle competition. We all ended up resorting to xgboost, like the vast majority of the participants if we believe the discussion forum. In addition, neural networks also seem to have played an essential part in the top contributions. Unfortunately, we have not spent much effort optimizing such models due to lack of time.


Duplicates make around 40% of the training set, and an estimated 15-20% of the testing set. Rebalancing the former set before training the models allowed to improve the score y about 10%.

Model ensembling

By the end of the challenge, we merged the models we had been building seperately until then, which significantly boosted our score. Model ensembling is an efficient technique which is well-known to Kagglers, but it is still fascinating to observe it in practice. A simple averaging of the predictions improved the overall score by about 10%. Using another xgboost on top of the individual models, which took the individual predictions as input features, allowed us to reduce the score even more. (To do this we had to set aside a part of the training set, train the base models on the remaning part and use the predictions on the subset left aside as input features to train the top layer xgboost.)

This raised hopes for further improvements by adding additional features alongside the predictions of the base layer models: we hoped that the top layer xgboost could thence identify in which areas of the features space each of the base models performed better, in order to fine-tune the aggregation. Much to our disappointment, this approach did not yield any improvement of the score despite our various attempts: addition of all features, of top features, of random features, of PCA-reduced features…


Participating to a Kaggle challenge proved to be a rich experience, which allowed our data scientists to push machine learning approaches to their limits. Think about model ensembling for example: this is not something we normally do in a production environment, so we learnt a lot by putting this in practice.

We hope we can clear some time again in a few months to participate to another challenge. Three things we will then do better: (1) start on time; (2) use neural networks; (3) master model ensembling with additional features.

See you soon!