Labeled text classification datasets are typically only available in a few select languages. In order to train a model for e.g news categorization in a language L t without a suitable text classification dataset there are two options. The first option is to create a new labeled dataset by hand, and the second option is to transfer label information from an existing labeled dataset in a source language L s to the target language L t . In this paper we propose a method for sharing label information across languages by means of a language independent text encoder. The encoder will give almost identical representations to multilingual versions of the same text. This means that labeled data in one language can be used to train a classifier that works for the rest of the languages. The encoder is trained independently of any concrete classification task and can therefore subsequently be used for any classification task. We show that it is possible to obtain good performance even in the case where only a comparable corpus of texts is available.
This paper addresses the problem of learning a cross-language text categorizer with no labelled information in the target language. The suggested solution relies on learning cross-lingual embeddings, and training a classifier using labelled data in the source language only.
The idea of using cross-lingual or multilingual representations to seamlessly handle documents across languages is not terribly novel as it has been use in multilignual categorization or semantic similarity for some time. This contribution however proposes a clean separation of the multiligual encoder and classifier, as well as a good (but long) section on related prior art.
One concern is that the modelling section stays fairly high level and is hardly sufficient, for example to re-implement the models. Many design decisions (e.g. #layers, #units) are not justified. They likely result from preliminary experiments, in that case it should be said.
The main concern is that the experiments could be greatly improved. Given the extensive related work section, it is odd that no alternate model is compared to. The details on the experiments are also scarce. For example, are all accuracy results computed on the same 8k test set? If so this should be clearly stated. Why are models tested on small subsets of the available data? You have 493k Italian documents, yet the largest model uses 158k... It is unclear where many such decisions come from -- e.g. Fig 4b misses results for 1000 and 1250 dimensions and Fig 4b has nothing between 68k and 137k, precisely where a crossover happens.
In short, it feels like the paper would greatly improve from a clearer modeling description and more careful experimental design.
Misc:
- Clarify early on what "samples" are in your categorization context.
- Given the data set, why use a single-label multiclass setup, rather than multilabel?
- Table 1 caption claims an average of 2.3 articles per topic, yet for 200 topics you have 500k to 1.5M articles?
- Clarify the use of the first 200 words in each article vs. snippets
- Put overall caption in Figs 2-4 on top of (a), (b), otherwise references like Fig 4b are unclear.