4th Place Solution for Mercari Price Suggestion Challenge on Kaggle

This is the 4th text mining competition I have attend on Kaggle. The other three are:

• CrowdFlower Search Results Relevance Competition, 1st Place
• Home Depot Product Search Relevance Competition, 3rd Place
• The Hunt for Prohibited Content Competition, 4th Place

In these previous competitions, I took the general ML based methods, i.e., data cleaning, feature engineering (see the solutions of CrowdFlower and HomeDepot for how many features have been engineered), VW/XGBoost training, and massive ensembling.

Since I have been working on CTR & KBQA based on deeplearning and embedding models for some time, I decided to give this competition a shot. With data of this competition, I have experimented with various ideas such as NN based FM and snapshot ensemble, which will be described in details below.

This repo describes our method for the above competition. Highlights of our method are as follows:

• very minimum preprocessing with focus on end-to-end learning with multi-field inputs (e.g., text, categorical features, numerical features);
• hybrid NN consists of four major compoments, i.e., embed, encode, attend and predict. FastText and NN based FM are used as building block;
• purely bagging of NNs of the same architecture via snapshot ensemble;
• efficiency is achieved via various approaches, e.g., lazynadam optimization, fasttext encoding and average pooling, snapshot ensemble, etc.

The design of the overall architecture of the model follows the well written post by Matthew Honnibal [1]. It mainly contains four parts as described below.

With FastText encoding [2], it just return the input unmodified.

TextCNN is based on [3]. It is time consuming based on our preliminary attempt. We did not go too far with it.

It is time consuming based on our preliminary attempt. We did not go too far with it.

Very time consuming.

See Reference [4]. It is very time consuming.

Simple and effective. Works ok with FastText encoder.

Simple but a litte worse than average pooling when works with FastText encoder.

We tried both context attention and self attention [5]. Context attention is very time consuming. Self attention helps to improve the results, but is also time consuming. At the end, we disable it.

We got many inspirations from the NN based FM models, e.g., NFM [6], AFM [7] and DeepFM [8]. Such FM models help to model the interactions between different fields, e.g., item_description and brand_name/category_name/item_condition_id.

See Reference [9]. We started out with ResNet in the very beginning of the competition. While it works ok, it's a bit slower than pure MLP.

See Reference [10]. We also tried DenseNet besides ResNet. However, the decrease of RMSLE is ver mimimum. In the end, we stick with pure MLP, which is efficient and effecive.

For loss, we tried mean squared loss and huber loss, and the later turned out to be a little bit better.

In the final solution, we use cyclic lr schedule and snapshot ensemble to produce efficient and effective ensmble with a single NN architecture described above.

[1] Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models

Not finalized @ 2018.02.23.