user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Multitask learning of Multilingual Sentence Representations

Paper content:
We present a novel multi-task training approach to learning multilingual distributed representations of text.
Our system learns word and sentence embeddings jointly by training a multilingual skip-gram model together with a cross-lingual sentence similarity model.
We construct sentence embeddings by processing word embeddings with an LSTM and by taking an average of the outputs.
Our architecture can transparently use both monolingual and sentence aligned bilingual corpora to learn multilingual embeddings, thus covering a vocabulary significantly larger than the vocabulary of the bilingual corpora alone.
Our model shows competitive performance in a standard cross-lingual document classification task.
We also show the effectiveness of our method in a low-resource scenario.
Learning distributed representations of text, whether it be at the level of words BID26 ; BID28 , phrases BID32 ; BID30 , sentences BID18 or documents BID22 , has been one of the most widely researched subjects in natural language processing in recent years.
Word/sentence/document embeddings, as they are now commonly referred to, have quickly become essential ingredients of larger and more complex NLP systems BID4 ; BID25 BID8 ; BID1 ; BID6 looking to leverage the rich semantic and linguistic information present in distributed representations.One of the exciting avenues of research that has been taking place in the context of distributed text representations, which is also the subject of this paper, is learning multilingual text representations shared across languages BID11 ; BID3 ; BID24 .
Multilingual embeddings open up the possibility of transferring knowledge across languages and building complex NLP systems even for languages with limited amount of supervised resources BID0 ; BID17 .
By far the most popular approach to learning multilingual embeddings is to train a multilingual word embedding model that is then used to derive representations for sentences and documents by composition BID14 .
These models are typically trained solely on word or sentence aligned corpora and the composition models are usually simple predefined functions like averages over word embeddings BID14 ; BID27 or parametric coposition models learned along with the word embeddings.In this work we learn word and sentence embeddings jointly by training a multilingual skip-gram model BID24 together with a cross-lingual sentence similarity model.
The multilingual skip-gram model transparently consumes (word, context word) pairs constructed from monolingual as well as sentence aligned bilingual corpora.
We use a parametric composition model to construct sentence embeddings from word embeddings.
We process word embeddings with a Bi-directional LSTM and then take an average of the LSTM outputs, which can be viewed as context dependent word embeddings.
Since our multilingual skip-gram and cross-lingual sentence similarity models are trained jointly, they can inform each other through the shared word embedding layer and promote the compositionality of learned word embeddings at training time.
Further, the gradients flowing back from the sentence similarity model can affect the embeddings learned for words outside the vocabulary of the parallel corpora.
We hypothesize these two aspects of our model lead to more robust sentence embeddings.Our contributions are as follows :• Scalable approach: We show that our approach performs better as more languages are added, since represent the extended lexicon in a suitable manner.•
Ability to perform well in low-resource scenario: Our approach produces representations comparable with the state-of-art multilingual sentence embeddings using a limited amount of parallel data. Our
sentence embedding model is trained end-to-end on a vocabulary significantly larger than the vocabulary of the parallel corpora used for learning crosslingual sentence similarity.• Amenable
to Multi-task modeling: Our model can be trained jointly with proxy tasks, such as sentiment classification, to produce more robust embeddings for downstream tasks.
Our results suggest that using a parametric composition model to derive sentence embeddings from word embeddings and joint multi-task learning of multilingual word and sentence embeddings are promising directions.
This paper is a snapshot of our current efforts and w e believe that our sentence embedding models can be improved further with straightforward modifications to the model architecture, for instance by using stacked LSTMs, and we plan to explore these directions in future work.In our exploration of architectures for the sentence encoding model, we also tried using a selfattention layer following the intuition that not all words are equally important for the meaning of a sentence.
However, we later realized that the cross lingual sentence similarity objective is at odds with what we want the attention layer to learn.
When we used self attention instead of simple averaging of word embeddings, the attention layer learns to give the entire weight to a single word in both the source and the target language since that makes optimizing cross lingual sentence similarity objective easier.Even though they are related tasks, multilingual skip-gram and cross-lingual sentence similarity models are always in a conflict to modify the shared word embeddings according to their objectives.
This conflict, to some extent, can be eased by careful choice of hyper-parameters.
This dependency on hyper-parameters suggests that better hyper-parameters can lead to better results in the multi-task learning scenario.
We have not yet tried a full sweep of the hyperparameters of our current models but we believe there may be easy gains to be had from such a sweep especially in the multi-task learning scenario.

assistant: text: We jointly train a multilingual skip-gram model and a cross-lingual sentence similarity model to learn high quality multilingual text embeddings that perform well in the low resource scenario.