Learning OOV through semantic relatedness in spoken dialog systems
Ming Sun, Yun-Nung Chen, Alexander I. Rudnicky · 2015
• Speech recognition and language understanding performance can be improved through an OOV expectand-learn procedure. • A limited domain vocabulary can be utilized to effectively acquire OOVs by the word relatedness theory through web knowledge bases. • With data-driven semantic relatedness, both the global and local learning procedures are able to successfully harvest more than 50% of OOVs, leading to better recognition and understanding performance. • This work demonstrates that o OOV learning may benefit dialog system o the proposed expect-and-learn strategy outperforms the traditional detect-and-learn in both higher effectiveness and no human involvement. 1. Linguistically semantic relatedness o Defined by linguistics, e.g., WordNet (WN), Paraphrase Database (PPDB) (Ganitkevitch et al., 2013) 2. Data-driven semantic relatedness o Distributional semantics, e.g., continuous bag-ofword embeddings (CBOW) (Mikolov et al., 2013) Detect-and-Learn (Qin et al., 2011; 2012): o Discover OOV words during the conversation o Example: S: “I heard something like SELF, can you repeat it?” U: “It’s SELFIE.” o Drawbacks • Limited number of new words • Required human efforts to correct spellings and pronunciations Expect-and-Learn (proposed): o Use semantic relatedness to automatically enrich the vocabulary and language model beforehand