2. Contents:
◦ Introdution
◦ Task Labels
◦ Approches to NLI
◦ Evaluation and Data sets
◦ Distributional and Vector Space Models
◦ Current State-of-the-Art Approaches
◦ Challenges and Future Direction
◦ Conclusion
3. Introduction
◦ NLI systems aim to bridge the gap between human language and machine understanding
by processing and interpreting natural language inputs.
◦ NLI is the task of determining the logical relationship between two pieces of text the premise
and the hypothesis.
◦ Goal is to understand whether the meaning of the hypothesis can be inferred from the
premise.
5. Task Labels
◦ Task labels in Natural Language Inference (NLI) refer to the categories or labels used to
classify the relationships between the premise and hypothesis pairs in an NLI dataset. These
labels indicate the nature of the relationship between the two sentences and are crucial
for training, evaluating, and analyzing NLI models. Common task labels in NLI include:
◦ Entailment:
◦ The hypothesis can be inferred or logically entailed by the premise. It implies that the
information in the hypothesis is supported or logically follows from the information in the
premise.
◦ Contradiction:
◦ The hypothesis contradicts or is logically inconsistent with the premise. It suggests that the
information in the hypothesis contradicts the information in the premise.
◦ Neutral:
◦ There is no logical relationship or strong inference between the premise and the
hypothesis. It implies that the hypothesis neither entails nor contradicts the premise.
6. Examples
Entailment
Premise: "The cat is on the mat.
" Hypothesis: "The mat is under the cat."
Task Label: Entailment
Contradiction
Premise: "The sun rises in the east."
Hypothesis: "The sun sets in the west.“
Task Label: Contradiction
Neutral
Premise: "She is wearing a blue dress.“
Hypothesis: "The sky is clear.“
Task Label: Neutral
7. Continue..
◦ Partial Entailment:
◦ The hypothesis partially entails or is partially supported by the premise. It indicates that
some but not all of the information in the hypothesis can be inferred from the premise.
◦ Partial Contradiction:
◦ The hypothesis partially contradicts or is partially inconsistent with the premise. It
suggests that some but not all of the information in the hypothesis contradicts the
information in the premise.
8. Example:
Partial Entailment
Premise: "The cat is sitting on the mat.“
Hypothesis: "There is a cat on the floor."
Task Label: Partial Entailment
Partial Contradiction
Premise: "The weather is sunny.“
Hypothesis: "It might rain later.“
Task Label: Partial Contradiction
9. Approches to NLI
◦ Rule-based Systems
Predefined grammatical rules and patterns to interpret and generate natural language.
◦ Template Filling
Templates contained predefined slots that were filled with extracted information from user
queries.
◦ Finite-State Automata
Provided a simple and efficient way to handle dialogue interactions but struggled with
handling context and ambiguity.
◦ Slot-Filling Approaches
Involved identifying specific slots or parameters in user queries and mapping them to
corresponding actions.
◦ Command-based Interfaces
They paved the way for more sophisticated techniques and approaches in modern NLI
systems
10. Evaluation and Data sets
They provide benchmarks to measure the performance and effectiveness of NLI models.
Here's an overview of evaluation methods and commonly used data sets in NLI
Evaluation Methods
1. Accuracy Metrics
2. Precision, Recall, and F1 Score
3. Error Analysis
11. Data Sets
1. ATIS (The Airline Travel Information System)
2. SNIPS (Spoken Language Understanding in Intelligent Personal Assistants)
3. NLU-Evaluation-Corpora
4. MultiWOZ
5. Squad
6. Custom Data Sets
12. Current State-of-the-Art Approaches
In the field of Natural Language Inference (NLI), several state-of-the-art approaches
have emerged in recent years, achieving remarkable performance on benchmark
datasets. Here are some notable approaches
1. BERT (Bidirectional Encoder Representations from Transformers)
2. RoBERTa (Robustly Optimized BERT Pretraining Approach)
3. ALBERT (A Lite BERT)
4. ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements
Accurately)
5. DeBERTa (Decoding-enhanced BERT with Disentangled Attention)
6. T5 (Text-to-Text Transfer Transformer)
14. Challenges and Future Directions
Challenges:
◦ Ambiguous Language.
◦ Cross-Domain Generalization.
◦ Handling Negation and Uncertainty.
◦ Incorporating World Knowledge.
◦ Lack of Diversity in Training Data.
Future Directions:
◦ Explainability and Interpretability.
◦ Multimodal NLI.
◦ Advances in Pre-training Techniques.
◦ Benchmarking and Evaluation.
15. Conclusion
◦ Natural Language Inference is an important task that makes us develop models that can actually
understand the dependencies between sentences.
◦ What we especially talk about ML these days is that transformers are ubiquitous. Thus, the models we
are also applicable in many applied tasks beyond NLI. Many transformer-based models are
benchmarked on NLI tasks to show the performance gains compared to the previous architectures.
16. References
◦ Bowman, S. R., Angeli, G., Potts, C., & Manning, C. D. (2015). A large annotated corpus for learning natural
language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language
Processing (EMNLP) (pp. 632-642).
◦ Conneau, A., Kiela, D., Schwenk, H., Barrault, L., & Bordes, A. (2017). Supervised learning of universal
sentence representations from natural language inference data. In Proceedings of the 2017 Conference on
Empirical Methods in Natural Language Processing (EMNLP) (pp. 670-680).
◦ Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers
for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the
Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (pp. 4171-4186).
◦ Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., ... & Stoyanov, V. (2019). RoBERTa: A robustly optimized
BERT pretraining approach. arXiv preprint arXiv:1907.11692
