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Title: Natural Language Inference over Interaction Space

Paper content:
Natural Language Inference (NLI) task requires an agent to determine the logical relationship between a natural language premise and a natural language hypothesis.
We introduce Interactive Inference Network (IIN), a novel class of neural network architectures that is able to achieve high-level understanding of the sentence pair by hierarchically extracting semantic features from interaction space.
We show that an interaction tensor (attention weight) contains semantic information to solve natural language inference, and a denser interaction tensor contains richer semantic information.
One instance of such architecture, Densely Interactive Inference Network (DIIN), demonstrates the state-of-the-art performance on large scale NLI copora and large-scale NLI alike corpus.
It's noteworthy that DIIN achieve a greater than 20% error reduction on the challenging Multi-Genre NLI (MultiNLI) dataset with respect to the strongest published system.
Natural Language Inference (NLI also known as recognizing textual entiailment, or RTE) task requires one to determine whether the logical relationship between two sentences is among entailment (if the premise is true, then the hypothesis must be true), contradiction (if the premise is true, then the hypothesis must be false) and neutral (neither entailment nor contradiction).
NLI is known as a fundamental and yet challenging task for natural language understanding , not only because it requires one to identify the language pattern, but also to understand certain common sense knowledge.
In TAB0 , three samples from MultiNLI corpus show solving the task requires one to handle the full complexity of lexical and compositional semantics.
The previous work on NLI (or RTE) has extensively researched on conventional approaches BID25 Bos & Markert, 2005; BID39 .
Recent progress on NLI is enabled by the availability of 570k human annotated dataset and the advancement of representation learning technique.Among the core representation learning techniques, attention mechanism is broadly applied in many NLU tasks since its introduction: machine translation BID15 , abstractive summarization BID50 , Reading Comprehension , dialog system BID41 , etc.
As described by BID57 , "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is computed as a weighted sum of the values, where the weight assigned to each value is computed by a compatibility function of the query with the corresponding key".
Attention mechanism is known for its alignment between representations, focusing one part of representation over another, and modeling the dependency regardless of sequence length.
Observing attention's powerful capability, we hypothesize that the attention weight can assist with machine to understanding the text.A regular attention weight, the core component of the attention mechanism, encodes the crosssentence word relationship into a alignment matrix.
However, a multi-head attention weightVaswani et al. (2017) can encode such interaction into multiple alignment matrices, which shows a more powerful alignment.
In this work, we push the multi-head attention to a extreme by building a word- by-word dimension-wise alignment tensor which we call interaction tensor.
The interaction tensor encodes the high-order alignment relationship between sentences pair.
Our experiments demonstrate that by capturing the rich semantic features in the interaction tensor, we are able to solve natural language inference task well, especially in cases with paraphrase, antonyms and overlapping words.We dub the general framework as Interactive Inference Network(IIN).
To the best of our knowledge, it is the first attempt to solve natural language inference task in the interaction space.
We further explore one instance of Interactive Inference Network, Densely Interactive Inference Network (DIIN), which achieves new state-of-the-art performance on both SNLI and MultiNLI copora.
To test the generality of the architecture, we interpret the paraphrase identification task as natural language inference task where matching as entailment, not-matching as neutral.
We test the model on Quora Question Pair dataset, which contains over 400k real world question pair, and achieves new state-of-the-art performance.We introduce the related work in Section 2, and discuss the general framework of IIN along with a specific instance that enjoys state-of-the-art performance on multiple datasets in Section
3. We describe experiments and analysis in Section
4. Finally, we conclude and discuss future work in Section 5.
We show the interaction tensor (or attention weight) contains semantic information to understand the natural language.
We introduce Interactive Inference Network, a novel class of architecture that allows the model to solve NLI or NLI alike tasks via extracting semantic feature from interaction tensor end-to-end.
One instance of such architecture, Densely Interactive Inference Network (DIIN), achieves state-of-the-art performance on multiple datasets.
By ablating each component in DIIN and changing the dimensionality, we show the effectiveness of each component in DIIN.Though we have the initial exploration of natural language inference in interaction space, the full potential is not yet clear.
We will keep exploring the potential of interaction space.
Incorporating common-sense knowledge from external resources such as knowledge base to leverage the capacity of the mode is another research goal of ours.

assistant: text: show multi-channel attention weight contains semantic feature to solve natural language inference task.