Concept-Based Embeddingsfor Natural Language Processing
Table 5.5: Performance of reranking model on TedLium corpus
['[EMPTY]', '[BOLD] WER', '[BOLD] WER (TF-IDF≥3)']
-Ch2S2SS5 -Chx2T1 0.4pt0.1pt It shows that the proposed discriminatively trained RBM produces greater WER reduction than baseline SLP rerankers. Effectiveness of using prior knowledge is validated by further improving WER over dRBM. The greatest absolute WER reduction (1.3%) is achieved by the late fusion of SLP and p-dRBM, which confirms that our reranker captures information complementary to SLP. In comparison, proposed RBM rerankers, especially p-dRBM, have reduced more errors on chosen keywords without sacrificing overall performance. We further break down the TF-IDF scores into 3 bins. Thanks to hidden variables, our methods are capable of better capturing the discriminative information for most content words. In particular, p-dRBM is shown working significantly better than other methods on words with medium TF-IDF scores (<5), which is a result of injecting named entity words, e.g., washington (TF-IDF=3.87), that mostly have TF-IDF between 3.0 and 5.0.