We present a probabilistic generative model for learning semantic parsers from ambiguous supervision. Our approach learns from natural language sentences paired with world states ...
Many state-of-the-art statistical parsers for English can be viewed as Probabilistic Context-Free Grammars (PCFGs) acquired from treebanks consisting of phrase-structure trees enri...
— Language and image understanding are two major goals of artificial intelligence which can both be conceptually formulated in terms of parsing the input signal into a hierarchi...
Long Zhu, Yuanhao Chen, Yuan Lin, Chenxi Lin, Alan...
We present MARS (Multilingual Automatic tRanslation System), a research prototype speech-to-speech translation system. MARS is aimed at two-way conversational spoken language trans...
Yuqing Gao, Bowen Zhou, Zijian Diao, Jeffrey S. So...
Many semantic parsing models use tree transformations to map between natural language and meaning representation. However, while tree transformations are central to several state-...