Abstract | Additionally, we show that a high—level planner utilizing these extracted relations significantly outperforms a strong, text unaware baseline — successfully completing 80% of planning tasks as compared to 69% for the baseline.1 |
Conclusions | We show that building high-level plans in this manner significantly outperforms traditional techniques in terms of task completion. |
Introduction | Our results show that our text-driven high-level planner significantly outperforms all baselines in terms of completed planning tasks — it successfully solves 80% as compared to 41% for the Metric-FF planner and 69% for the text unaware variant of our model. |
Abstract | Experiments on Chinese—English translation on four NIST MT test sets show that the HD—HPB model significantly outperforms Chiang’s model with average gains of 1.91 points absolute in BLEU. |
Experiments | Table 3 shows that our HD-HPB model significantly outperforms Chiang’s HPB model with an average improvement of 1.91 in BLEU (and similar improvements over Moses HPB). |
Introduction | Experiments on Chinese-English translation using four NIST MT test sets show that our HD-HPB model significantly outperforms Chiang’s HPB as well as a SAMT—style refined version of HPB. |
Abstract | We show empirically that TESLA—CELAB significantly outperforms character-level BLEU in the English—Chinese translation evaluation tasks. |
Conclusion | We show empirically that TESLA-CELAB significantly outperforms the strong baseline of character-level BLEU in two well known English-Chinese MT evaluation data sets. |
Experiments | The results indicate that TESLA-CELAB significantly outperforms BLEU. |
Experimental Results | On head queries, the addition of the empty context parameter 0 and click signal (2 together (Model M1) significantly outperforms both the baseline and the state-of-the-art model Guo’ 09. |
Experimental Results | We observe a different behavior on tail queries where all models significantly outperform the baseline BFB, but are not significantly different from each other. |
Introduction | We show that jointly modeling user intent and entity type significantly outperforms the current state of the art on the task of entity type resolution in queries. |