[
Table 11: Breaking NLI scores (accuracy %). Results marked with * as reported by Glockner et al. (2018). InferSent results obtained with our implementation using the training set-up described in (Conneau et al. 2017). Scores highlighted with bold are top scores when comparing the InferSent and our HBMP model.
['[BOLD] Category', '[BOLD] Decomp.  [BOLD] Attn*', '[BOLD] ESIM*', '[BOLD] WordNet  [BOLD] Baseline*', '[BOLD] KIM*', '[BOLD] Infer-  [BOLD] Sent', '[BOLD] 600D  [BOLD] HBMP']
Our model also compares well against the other models, outperforming Decomposable Attention model (51.90%) (Parikh et al. As these models are not based purely on sentence embeddings, the obtained result highlights that sentence embedding approaches can be competitive when handling inferences requiring lexical information.