Recent Advances in Natural Language Inference:A Survey of Benchmarks, Resources, and Approaches
Table 2: Comparison of exact-match accuracy achieved on selected benchmarks by a random or majority-choice baseline, various neural contextual embedding models, and humans. ELMo refers to the highest-performing listed approach using ELMo embeddings. Best system performance on each benchmark in bold. Information extracted from leaderboards (linked to in the first column) at time of writing (October 2019), and original papers for benchmarks introduced in Section 2.
['[BOLD] Benchmark', '[BOLD]  Simple Baseline ', '[BOLD] ELMo', '[BOLD] GPT', '[BOLD] BERT', '[BOLD] MT-DNN', '[BOLD] XLNet', '[BOLD] RoBERTa', '[BOLD] ALBERT', '[BOLD] Human']
The most representative models are ELMO, GPT, BERT and its variants, and XLNET. Next, we give a brief overview of these models and summarize their performance on the selected benchmark tasks. Table 2 quantitatively compares the performance of these models on various benchmarks. [CONTINUE] smaller tweaks to various aspects of the model have resulted in hundreds of entries on leaderboards (e.g., those linked to in Section 4.3.3 and Table 2) leading only to marginal improvements.