Papers with SciTail
A logical-based corpus for cross-lingual evaluation (D19-61)
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| Challenge: | a recent study shows that deep learning models can be used to solve textual inference tasks using simple linguistic patterns. |
| Approach: | They propose a set of syntactic tasks focused on contradiction detection that exploit linguistic patterns. |
| Outcome: | The proposed tasks can be implemented in English and Portuguese. |
End-Task Oriented Textual Entailment via Deep Explorations of Inter-Sentence Interactions (P18-2)
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| Challenge: | Existing datasets for textual entailment (TE) have been used to study TE. |
| Approach: | They propose a deep explorations of inter-sentence interactions for textual entailment task that uses a convolution to make important words in P and H play a dominant role in learnt representations. |
| Outcome: | Experiments show that the pretrained DEISTE on SciTail gets 5% improvement over prior state of the art and that it generalizes well on RTE-5. |
Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference (D18-1)
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| Challenge: | Using a new architecture, alignment pairs are compared, compressed and then propagated to upper layers for enhanced representation learning. |
| Approach: | They propose a new architecture where alignment pairs are compared, compressed and then propagated to upper layers for enhanced representation learning. |
| Outcome: | The proposed architecture achieves competitive performance on three popular benchmarks, SNLI, MultiNLI and SciTail, while maintaining lightweight parameter size. |
SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization (2020.acl-main)
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| Challenge: | Existing methods for fine-tuning pre-trained models fail to generalize to unseen data. |
| Approach: | They propose a framework for robust and efficient fine-tuning for pre-trained models . proposed framework achieves new state-of-the-art performance on a number of NLP tasks . |
| Outcome: | The proposed framework outperforms the state-of-the-art T5 model on GLUE, SNLI, SciTail and ANLI. |
AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples (P18-1)
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| Challenge: | Recent deep learning entailment systems have achieved close to human level performance on large datasets, but the problem is far from solved. |
| Approach: | They propose a knowledge-guided adversarial example generator for incorporating large lexical resources into entailment models via only a handful of rule templates and a natural language example generator that iteratively adjusts to the discriminator’s weaknesses. |
| Outcome: | The proposed methods increase accuracy by 4.7% on SciTail and 2.8% on a 1% sub-sample of SNLI. |
Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching (D19-1)
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| Challenge: | Sentence matching is a key issue in natural language inference and paraphrase identification. |
| Approach: | They propose a semantics-oriented attention and deep fusion network (OSOA-DFN) that is oriented to the original semantic representation of another sentence and propagates attention information at each matching layer. |
| Outcome: | The proposed model can model sentence matching more precisely on three sentence matching benchmark datasets. |
Asynchronous Deep Interaction Network for Natural Language Inference (D19-1)
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| Challenge: | Existing methods have framed the reasoning problem as a semantic matching task. |
| Approach: | They propose an asynchronous deep interaction network (ADIN) to deconstruct the reasoning process and implement asynchron and multi-step reasoning. |
| Outcome: | The proposed model outperforms strong baselines on three popular benchmarks: SNLI, MultiNLI, and SciTail. |
Multi-Task Deep Neural Networks for Natural Language Understanding (P19-1)
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| Challenge: | Existing approaches to learning vector-space representations of text are multitask learning and language model pre-training. |
| Approach: | They propose a multi-task deep neural network (MT-DNN) that leverages cross-task data and incorporates a pre-trained bidirectional transformer language model. |
| Outcome: | The proposed model achieves state-of-the-art on ten NLU tasks and pushes the GLUE benchmark to 82.7% (2.2% absolute improvement) |
Knowledge-Enhanced Natural Language Inference Based on Knowledge Graphs (2020.coling-main)
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| Challenge: | Existing approaches to natural language inference rely on semantic knowledge, but background knowledge is limited to a few specific types. |
| Approach: | They propose a Knowledge Graph-enhanced NLI model that leverages background knowledge stored in knowledge graphs to facilitate inference. |
| Outcome: | The proposed model can leverage background knowledge stored in knowledge graphs to perform the task. |