| 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. |
Similar Papers
Convolutional Interaction Network for Natural Language Inference (D18-1)
Copied to clipboard
| Challenge: | Attention-based neural models have achieved great success in natural language inference (NLI). |
| Approach: | They propose a general model to capture the interaction between two sentences, which can be an alternative to the attention mechanism for NLI. |
| Outcome: | The proposed model can capture complex interactions on three large datasets. |
Asymmetric feature interaction for interpreting model predictions (2023.findings-acl)
Copied to clipboard
| Challenge: | Prior work on feature interaction attribution studies focus on asymmetric interaction that only explains the additional influence of a set of words in combination, which fails to capture asymmetry influence that contributes to model prediction. |
| Approach: | They propose an asymmetric feature interaction attribution explanation model that explores asymmetry higher-order feature interactions in the inference of deep neural NLP models. |
| Outcome: | The proposed model outperforms state-of-the-art models on two sentiment classification datasets. |
Deep Learning for Natural Language Inference (N19-5)
Copied to clipboard
| Challenge: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning for language understanding and reasoning. |
| Approach: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development and cutting- edge deep learning models. |
| Outcome: | This tutorial discusses cutting-edge research on NLI, including recent advance on dataset development, cutting- edge deep learning models, and highlights from recent research on using NLI to understand capabilities and limits of deep learning model for language understanding and reasoning. |
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference (N18-1)
Copied to clipboard
Reza Ghaeini, Sadid A. Hasan, Vivek Datla, Joey Liu, Kathy Lee, Ashequl Qadir, Yuan Ling, Aaditya Prakash, Xiaoli Fern, Oladimeji Farri
| Challenge: | Existing approaches to natural language inference rely on simple reading mechanisms for independent encoding of the premise and hypothesis. |
| Approach: | They propose a novel bidirectional dependent reading network to efficiently model the relationship between a premise and a hypothesis during encoding and inference. |
| Outcome: | The proposed model outperforms existing methods by a considerable margin on the Stanford Natural Language Inference (SNLI) dataset. |
DocNLI: A Large-scale Dataset for Document-level Natural Language Inference (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing studies focus on sentence-level inference, which limits its application in downstream NLP problems. |
| Approach: | They propose to construct a large-scale dataset for document-level NLI that can be used to study NLP problems. |
| Outcome: | The proposed model performs well on popular sentence-level benchmarks and generalizes well to out-of-domain NLP tasks that rely on inference at document granularity. |
Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in modeling and datasets demonstrate promising performance for NLI. |
| Approach: | They explore the direct zero-shot applicability of NLI models to real applications . they analyze the robustness of models to longer and out-of-domain inputs . |
| Outcome: | The proposed models are robust to longer and out-of-domain inputs and can perform on full documents. |
Dual Inference for Improving Language Understanding and Generation (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have exploited the duality of the task pairs in machine translation and speech recognition. |
| Approach: | They propose to leverage the duality in the inference stage without retraining whole models. |
| Outcome: | The proposed method is effective in both NLU and NLG tasks, providing the great potential of practical use. |
MorphNLI: A Stepwise Approach to Natural Language Inference Using Text Morphing (2025.findings-naacl)
Copied to clipboard
| Challenge: | Existing models fail to capture important semantic features of logic such as monotonicity and negation. |
| Approach: | They propose a modular step-by-step approach to natural language inference . they use a language model to generate edits to incrementally transform the premise into the hypothesis . |
| Outcome: | The proposed method outperforms baseline models in realistic cross-domain settings with improvements up to 12.6% (relative). |
SciNLI: A Corpus for Natural Language Inference on Scientific Text (2022.acl-long)
Copied to clipboard
| Challenge: | Existing Natural Language Inference (NLI) datasets are not related to scientific text. |
| Approach: | They propose a large dataset for NLI that captures the formality in scientific text and contains 107,412 sentence pairs extracted from scholarly papers on NLP and computational linguistics. |
| Outcome: | The proposed model achieves a Macro F1 score of only 78.18% and an accuracy of 78.23%. |
A Neural-Symbolic Approach to Natural Language Understanding (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained language models have enabled deep neural networks to perform natural language understanding tasks, but their performance can drastically deteriorate when logical reasoning is needed. |
| Approach: | They propose a framework for NLU based on analogical reasoning based upon neural processing and logical reasoning using both neural and symbolic processing. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two NLU tasks, question answering (QA) and natural language inference (NLI). |