| 5 Feb 2017 |
 @Spacex:matrix.org | Certainly | 16:32:43 |
| @Spacex:matrix.org | HMM for example | 16:34:24 |
 Vincent | Well, do u think deep learning models are the solution to text comprehension? | 16:37:13 |
| Vincent | A lot of experts think natural language understanding is not probabilistic | 16:37:59 |
| Vincent | For example, Lstm rnn is just a sequence mapping function so it can't understand the meaning without the context | 16:39:07 |
| Vincent | Hence, it is only for simple translation only. | 16:39:31 |
 suriyadeepan | hello | 16:40:34 |
| suriyadeepan | there are complex models built with lstm that considers context, while decoding/answering/generating | 16:41:13 |
| @Spacex:matrix.org | I think pure engineering techniques and probability models have already failed to solve the core of natural languages complex problem ..the use of phoneme is the main issue for the failure of previous systems and will not allow more progress toward understanding / cognition or the creation of a complete intelligent agent | 16:48:52 |
| Vincent | I heard that semantic network is a major breakdown in this area | 16:51:06 |
| Vincent | It will solve the issue deep learning can't for language | 16:51:37 |
| @Spacex:matrix.org | Deep learning has made good progress so far but I need the knowledge representation part has not been deeply discussed | 16:53:17 |
| @Spacex:matrix.org | Thank you Vincent you just indicated one problem | 16:54:08 |
| @Spacex:matrix.org | When it comes to language understanding .. Design is the main goal | 16:57:50 |
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