Srinivas Bangalore
Biographic Data
| ID | 6536869 |
|---|---|
| NAME | Srinivas Bangalore |
| GIVEN NAMES | Srinivas |
| FAMILY NAME | Bangalore |
| SIGNATURE | BANGALORE S |
| AFFILIATIONS | Shannon Laboratory, AT&T Labs, 180 Park Avenue, Florham Park, NJ 07932 |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 2 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2000 |
| LATEST PUBLICATION YEAR | 2009 |
| H-INDEX | 1 |
Robust Understanding in Multimodal Interfaces
Multimodal grammars provide an effective mechanism for quickly creating integration and understanding capabilities for interactive systems supporting simultaneous use of multiple input modalities. However, like other approaches based on hand-crafted grammars, multimodal grammars can be brittle with respect to unexpected, erroneous, or disfluent input. In this article, we show how the finite-state approach to multimodal language processing can be …
Learning Dependency Translation Models as Collections of Finite-State Head Transducers
The paper defines weighted head transducers, finite-state machines that perform middle-out string transduction. These transducers are strictly more expressive than the special case of standard left-to-right finite-state transducers. Dependency transduction models are then defined as collections of weighted head transducers that are applied hierarchically. A dynamic programming search algorithm is described for finding the optimal transduction of …
Learning Dependency Translation Models as Collections of Finite-State Head Transducers
The paper defines weighted head transducers, finite-state machines that perform middle-out string transduction. These transducers are strictly more expressive than the special case of standard left-to-right finite-state transducers. Dependency transduction models are then defined as collections of weighted head transducers that are applied hierarchically. A dynamic programming search algorithm is described for finding the optimal transduction of …
Learning Dependency Translation Models as Collections of Finite-State Head Transducers
The paper defines weighted head transducers, finite-state machines that perform middle-out string transduction. These transducers are strictly more expressive than the special case of standard left-to-right finite-state transducers. Dependency transduction models are then defined as collections of weighted head transducers that are applied hierarchically. A dynamic programming search algorithm is described for finding the optimal transduction of …
Robust Understanding in Multimodal Interfaces
Multimodal grammars provide an effective mechanism for quickly creating integration and understanding capabilities for interactive systems supporting simultaneous use of multiple input modalities. However, like other approaches based on hand-crafted grammars, multimodal grammars can be brittle with respect to unexpected, erroneous, or disfluent input. In this article, we show how the finite-state approach to multimodal language processing can be …
Artificial Intelligence (2 works) · Computer Science (2 works) · Machine translation (2 works) · Natural Language Processing Techniques (2 works) · Topic Modeling (2 works) · Acoustics (1 work) · Algorithm (1 work) · Dependency (UML (1 work) · Finite-state machine (1 work) · Gesture (1 work)