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Title: Sentence embedding with contrastive multi-views learning

Paper content:
In this work, we propose a self-supervised method to learn sentence representations with an injection of linguistic knowledge.
Multiple linguistic frameworks propose diverse sentence structures from which semantic meaning might be expressed out of compositional words operations.
We aim to take advantage of this linguist diversity and learn to represent sentences by contrasting these diverse views.
Formally, multiple views of the same sentence are mapped to close representations.
On the contrary, views from other sentences are mapped further.
By contrasting different linguistic views, we aim at building embeddings which better capture semantic and which are less sensitive to the sentence outward form.
We propose to learn sentence embeddings by contrasting multiple linguistic representations.
The motivation is to benefit from linguistic structures diversity to discard noises inherent to each representation.
We aim at encoding high-level representations by aligning the underlying shared information from multiple views.
As illustrated in Figure 1 , we train our model with a contrastive framework which aims at mapping close input sentences to close representations while separating unrelated sentences.
In Natural Language Processing (NLP), this framework has been widely used to learn word representations (Mikolov et al., 2013a; b) for example.
This model relies on the distributional hypothesis which conjectures that words within similar context share similar meaning.
Such framework has also been extended to sentences with the similar hypothesis that the meaning can be inferred from the context sentences (Logeswaran & Lee, 2018; .
We propose to extend this framework by assuming that different views of the same sentence should lead to close representation.
We considered the dependency trees, a linguistic framework that describes the compositional structure of a sentence.
As illustrated in Figure 1 , in this framework, the sentence is mathematically described as an oriented acyclic graph where the nodes are words and edges describe the relations between words.
Such structure has benefited from an important attention in the NLP community and efficient parser tools for various languages are available, which makes it possible to obtain such information almost freely in the sense it does not require additional hand annotated data.
Tree representations are then mapped in a shared embedding space using appropriate Tree LSTM networks introduced in Tai et al. (2015) .
Model parameters are learned using a discriminating objective as proposed in Logeswaran & Lee (2018) .
We exploit the diversity of linguistic structures to build sentence representations.
Out method shows promising results and does not require hand annotated data.
More scalable implementations might be considered to explore more experimentation setups.
Although results are below state of the art performances, our model is trained on only a small proportion of the bookcorpus sentences as stated in Figure 2 .
A larger exposition to the training data and an extended training time might benefit to the downstream and probing scores.
Other linguistic structures might also be tested such as constituency tree associated with N-ary Tree LSTM or Tree LSTM improved with an attention mechanism.
A COMPUTING METHOD FOR TREE LSTM
We implemented a batching procedure to fasten Tree LSTM computations.
Group of nodes are computed sequentially to insure all node children have already been computed.
Nodes are considered given their distance to the root node.
First, Leaf nodes with highest depth are computed to progressively compute inner nodes.
The Tree LSTM cell implementation is specifically designed to treat simultaneously all nodes in each the step.
Figure 4: The batching procedure to optimize the graph computation.
For each batch, the computation is decomposed in steps which insure that every node dependent have already been computed.
At each step, node with the same depth to the root are computed in a single operation and the output fed to the next computational step.

assistant: text: We aim to exploit the diversity of linguistic structures to build sentence representations.