8 ms·

An Upgrade to SyntaxNet, New Models and a Parsing Competition

325 points by liviosoares 10y ago

webmaven 10y ago
Very interesting release. The bit about guessing the part of speech, stem, etc. for previously unseen words should (I think) make it much more useful in contexts that succumb to neologizing, verbing nouns, nouning verbs, and so on (such as business writing, technical writing, academic papers, science fiction & fantasy, slang, etc.). I wonder how well it would do at parsing something that seems deliberately impenetrable, like TimeCube rants, or postmodern literary criticism.
- PeterisP 10y ago
  It's much more useful in all contexts - every problem/task has a 100 words that are very common and important there while being rare and unknown in general; the problem is that for every niche that's different 100 terms.
  - webmaven 10y ago
    Right, except in terms of neologizing I was referring to contexts where many individual texts are trying to establish a new term. So if you are trying to parse Science Fiction texts, yes there are "terms of art" that don't appear outside of that field (eg. "blaster"), but often there are terms that don't appear anywhere else, not even in other works by the same author. Other pathological cases are business books trying to coin a term or twist existing words into new meanings (eg. "cloud"), verbing nouns (incentivize), nouning verbs (likes, learnings), and so on.
pbnjay 10y ago
This looks amazing. I'm especially curious how well it will work at identifying Gene/chemical nomenclature since it is fairly consistent like English spelling. For named entity recognition in biomedical text this could be really useful!
qeternity 10y ago
Given how many areas NLP can be applied to, I can only imagine all of these future internal project proposals where someone has to explain to some C-suite exec how they are going to revolutionize the business with Parsey McParseface. Or better yet, when they have to budget a big upgrade to the "DRAGNN based ParseySaurus". Fun times ahead.
- frahs 10y ago
  there's probably a cooler internal name for it.
- ucaetano 10y ago
  Sounds like a way to troll other organizations and competitors, imagine the conversations: -(Eng) We need to switch to this new NLP framework - (VP) Ok, why? Which one is it? - (Eng) Huh, it's called Parsey McParseface, developed by ... - (VP) WTF? Don't waste my time with jokes, go build your own - (Eng) But ... - (VP) Meeting's over.
minimaxir 10y ago
See also: spaCy, which is an open-source NLP framework that has some integration with Keras as well: https://news.ycombinator.com/item?id=13874787 https://news.ycombinator.com/item?id=13874787 ...and apparently will release a major version update today. Ouch.
- binarymax 10y ago
  I don't think spaCy will be hurting any time soon. When SyntaxNet was first released last year, Matthew Honnibal had a good writeup [0] of how spaCy vastly outperforms with speed while keeping reasonable accuracy: >On the time-honoured benchmark for this task, Parsey McParseface achieves over 94% accuracy, at around 600 words per second. On the same task, spaCy achieves 92.4%, at around 15,000 words per second. The extra accuracy might not sound like much, but for applications, it's likely to be pretty significant. If spaCy is able to increment the accuracy and maintain the large performance gap, it'll still be my go-to NLP framework! [0] https://explosion.ai/blog/syntaxnet-in-context https://explosion.ai/blog/syntaxnet-in-context
- syllogism 10y ago
  Why ouch? :). It's not like there's a zero-sum game here. It's great to see more things being released, so the ecosystem can continue to improve. I do wish SyntaxNet were a bit easier to use. A lot of people have asked for SyntaxNet as a backend for spaCy, and I'd love to be using it in a training ensemble. When I tried this last year, I had a lot of trouble getting it to work as a library. I spent days trying to pass it text in memory from Python, and it seemed like I would have to write a new C++ tensorflow op. Has anyone gotten this to work yet?
  - Tpt 10y ago
    There is https://github.com/livingbio/syntaxnet_wrapper https://github.com/livingbio/syntaxnet_wrapper that does the job fairly well (I also spent days trying to be able to pass to SyntaxNet different textes without having to reload the model). Warning: installation is a bit difficult.
- dharma1 10y ago
canada_dry 10y ago
Hoping this will quickly make into someone's home grown self-hosted version of Alexa. Alexa, turn the lights on in the kitchen. Alexa, turn on the kitchen light. Alexa, light up the kitchen. Should all accomplish the same task using this framework.
- axiom92 10y ago
  I see your point, but the tasks that you have listed (and more difficult variants) can be easily handled using rule-based systems.
  - losteric 10y ago
    If I recall correctly, most of Alexa is simple rule-based systems.
- buro9 10y ago
  > Alexa, light up the kitchen May also be interpreted as: Alexa, set fire to the kitchen
- squeaky-clean 10y ago
  I've been slowly working on my own simple home "Alexa" using mostly CMUSphinx for the voice detection. Honestly my most successful methods involved the least amount of complex NLP. Just simply treating the sentence as a bag of words and looking for "on" or "off" or "change" (and their synonyms) and the presence of known smart objects works extremely well. I could say "Hey Marvin, turn on the lights and TV", or "Hey Marvin, turn the lights and TV on", or even "Hey Marvin, on make lights and TV." (It's named Marvin it after the android from The Hitchhiker's Guide, my eventual goal is to have it reply with snarky/depressed remarks). Adding 30 seconds of "memory" of the last state requested also made it seem a million times smarter and turns requests into a conversation rather than a string of commands. If it finds a mentioned smart object with no state mentioned, it assume the previous one. "Hey Marvin, turn on the lights." lights turn on "The TV too." tv turns on The downside to this approach is I would be showing it off to friends, and it could mis trigger. "Marvin turn off the lights." lights turn off "That's so cool, so it controls your TV, too?" TV turns off But it was mostly not an issue in real usage. Ultimately I've got the project on hold for now because I can't find a decent, non-commercial way of converting voice to text. I'd really rather not send my audio out to Amazon/Google/MS/IBM. Not just because of privacy, but cost and "coolness" factor (I want as much as possible processed locally and open-source). CMUSphinx's detection was mostly very bad. I couldn't even do complex NLP if I wanted because it picks up broken/garbled sentences. I currently build a "most likely" sentence by looping through sphinx's 20 best interpretations of the sentence and grabbing all the words that are likely to be commands or smart objects. I tried setting up Kaldi, but didn't get it working after a weekend and haven't tried again since. I don't really know any other options to use aside from CMUSphone, Kaldi, or a butt SaaS. I've wanted to add a text messaging UI layer to it. Maybe I'll use that as an excuse to try playing with ParseySaurus.
devy 10y ago
"Python 3 support is not available yet." [1]. It's only supported in Python 2.7, Why? [1] https://github.com/tensorflow/models/tree/master/syntaxnet https://github.com/tensorflow/models/tree/master/syntaxnet
- RussianCow 10y ago
  Probably because Google still mostly uses Python 2.7 internally.
  - devy 10y ago
    Yikes! Not being to run on Py3k is a deal breaker for me.
    - akinalci 10y ago
      It's definitely an unfortunate situation. The community has been coalescing around Python 3 in the last couple years, but Google is obviously encumbered by all its legacy Python 2.7 code. Their SyntaxNet library still doesn't have Python 3 support a year after release. I'm wondering what their long-term plans are given 2.7 EOL in 2020.
- deleted 10y ago
  [deleted]
- jorgemf 10y ago
  Only for the models, the core works with python 3. I tweaked the models to use them in python 3, it is things like 'xrange and range'.
  - dmorr 10y ago
    Check in a python 3 version of the code? The world will thank you!
jacquesm 10y ago
I've been fighting Tensorflow in the last couple of days to try an application on it, never before have I seen such a convoluted build process and a maze of dependencies. The best manual on getting tensorflow with CUDA support up and running is here: http://www.nvidia.com/object/gpu-accelerated-applications-tensorflow-installation.html http://www.nvidia.com/object/gpu-accelerated-applications-te... But it is a little bit out of date when it comes to version numbers. If you're going to try TensorBox (https://github.com/TensorBox/TensorBox https://github.com/TensorBox/TensorBox) it will get a bit harder still because of conflicts and build issues with specific versions of TensorFlow. There has to be an easier way to distribute a package. That said, all this is super interesting and Google really moved the needle by opensourcing TensorFlow and other ML packages.
- jorgemf 10y ago
  It is not a good idea to compile TensorFlow by your own unless you really need it (for example for TensorFlow serving). Python packages are the way to go.
  - jacquesm 10y ago
    I have to because I'm trying to use TensorBox which does not play well with the regular version of tensorflow that you can get pre-compiled. See this issue: https://github.com/TensorBox/TensorBox/issues/100 https://github.com/TensorBox/TensorBox/issues/100 and https://github.com/TensorBox/TensorBox/issues/102 https://github.com/TensorBox/TensorBox/issues/102 So then we're full-circle and installing from pip which doesn't work :( sigh. Anyway, I'll get it to work, somehow.
    - jorgemf 10y ago
      Those issues are related with TensorFlow <1.0. It wasn't an stable release. Try 1.0.1.
  - aseipp 10y ago
    This is a bit unfortunate, in a real sense. I mean, I already build enough software, so I'm not sad on missing out. But here's the thing: TensorFlow actually installed great on Windows and it took less than 10 minutes to get running, once I had Python3 installed, even with GPU support. Even worked awesome in VS Code, out of the box, with autocomplete in the python mode. Even a baby like me got started easily. But it's a bit disappointing to hear that the build system is something of a nightmare, if I ever wanted to contribute myself. There's always plenty of things to help with, I don't care about the cutting edge of machine learning (I'm happy to submit docs, examples, etc)... Then again, the TF people can't just nerd around on their build system, for dorks like me to maybe write some patches every once in a while. Always great to make it easier, though.
Winblows69 10y ago
Overall, do you think Google does more good than harm?
deleted 10y ago
[deleted]
tlow 10y ago
For those of us who aren't developers but maybe more aptly called "hackers" (cause we hack stuff together even though we're operating out of our league, sometimes we get stuff to work). I am wondering, is there a even higher level guide to using Tensor Flow. I am currently growing Sweet Peas in my office in enclosed containers that automanage environment, nutrition and water. I have the capaability to log a lot of data from a lot of sensors, including images. I have _no idea_ how I would even get started using Tensor Flow, but it would be cool if I could run experiments on environmental conditions and find optimal conditions for this sweet pea cultivar. Maybe I'm talking nonsense. Let me ask a more basic question, how might one log and create data for use with Tensor Flow. How might Tensor Flow be applied to robotic botanical situations?
- minimaxir 10y ago
  The short answer is to skip TensorFlow entirely and use/learn Keras for a high-level overview; then you can learn top-down if you need to use/look at TF code directly. Another HN thread has good tutorials for simple uses of Tensorflow: https://news.ycombinator.com/item?id=13464496 https://news.ycombinator.com/item?id=13464496 However, NNs are optimal for text/image data as they can learn the features. If your data features are already known, you don't necessarily need to use Tensorflow/Keras at all, and you'll have a easier time using conventional techniques like linear/logistic regression and xgboost.
  - Houshalter 10y ago
    sklearn has this flowchart for what machine learning method to use: http://scikit-learn.org/stable/_static/ml_map.png http://scikit-learn.org/stable/_static/ml_map.png
    - minimaxir 10y ago
      The flowchart predates NNs/GBTs which are Swiss-army knives, which is another reason why using either of them is sometimes considered cheating.
camdenlock 10y ago
> and to allow neural-network architectures to be created dynamically during processing of a sentence or document. Oh lord, is this the spark that lights the google skynet powder keg
- PeterisP 10y ago
  Not yet, you'd need to apply the same approach to agentive (decisions on how to act) problems as opposed to classification tasks; then you'd have the spark that lights the google skynet powder keg.
jakekovoor 10y ago
This is definitely a game changer! It's a very interesting research carried out by Google's research team and I believe this will be especially beneficial for future speech translation algorithms that would bring us a whole new, fresh experience with the way we converse with Alexa, Google Home, Siri, and many more. If you need to install TensorFlow onto your Windows 10 computer then here's a great guide which I have followed quiet a few times. :) http://saintlad.com/install-tensorflow-on-windows/ http://saintlad.com/install-tensorflow-on-windows/
dang 10y ago
We changed the title from "Google open-sources Tensorflow-based framework for NLP", which appears misleading, given that it happened last May: https://news.ycombinator.com/item?id=11686029 https://news.ycombinator.com/item?id=11686029. On HN the idea is to rewrite titles only to make them less misleading (or less baity). Please see https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html.
- liviosoares 10y ago
  dang, sorry if this seemed misleading. In my humble opinion, the blog title does not do full justice to the new release, primarily since it carries a new framework within SyntaxNet: https://github.com/tensorflow/models/blob/master/syntaxnet/g3doc/DRAGNN.md https://github.com/tensorflow/models/blob/master/syntaxnet/g... This new DRAGNN framework is what I thought the folks here would want to know. Perhaps I should have linked to the github page, rather than the blog announcement.
  - dang 10y ago
    Ah, I see. Probably a post pointing to that framework would have been a better idea. It never fails to surprise me, but discussion tends to be directed almost entirely by what's in a submission title. For the same reason, it probably doesn't make sense to change the current thread to point to that Github page now, since that would orphan the existing discussion.
coding123 10y ago
I've been rummaging through the docs on DRAGONN but can't seem to find proper installation/run instructions. There is a Google Cloud installation, but I want to just run on my laptop for now against the pre-trained files. Can't seem to get started with DRAGONN.
- coding123 10y ago
  Thank you thank you Ivan Bogatyy for fixing the docker run instructions. I think it was missing the docker image name before! :)
David23 10y ago
I have tried to run Syntaxnet as library but I found it very difficult (lot of dependencies).

325 points by liviosoares 10y ago

Very interesting release. The bit about guessing the part of speech, stem, etc. for previously unseen words should (I think) make it much more useful in contexts that succumb to neologizing, verbing nouns, nouning verbs, and so on (such as business writing, technical writing, academic papers, science fiction & fantasy, slang, etc.). I wonder how well it would do at parsing something that seems deliberately impenetrable, like TimeCube rants, or postmodern literary criticism.

PeterisP 10y ago
It's much more useful in all contexts - every problem/task has a 100 words that are very common and important there while being rare and unknown in general; the problem is that for every niche that's different 100 terms.
- webmaven 10y ago
  Right, except in terms of neologizing I was referring to contexts where many individual texts are trying to establish a new term. So if you are trying to parse Science Fiction texts, yes there are "terms of art" that don't appear outside of that field (eg. "blaster"), but often there are terms that don't appear anywhere else, not even in other works by the same author. Other pathological cases are business books trying to coin a term or twist existing words into new meanings (eg. "cloud"), verbing nouns (incentivize), nouning verbs (likes, learnings), and so on.

pbnjay 10y ago

This looks amazing. I'm especially curious how well it will work at identifying Gene/chemical nomenclature since it is fairly consistent like English spelling. For named entity recognition in biomedical text this could be really useful!

qeternity 10y ago

Given how many areas NLP can be applied to, I can only imagine all of these future internal project proposals where someone has to explain to some C-suite exec how they are going to revolutionize the business with Parsey McParseface. Or better yet, when they have to budget a big upgrade to the "DRAGNN based ParseySaurus". Fun times ahead.

frahs 10y ago
there's probably a cooler internal name for it.
ucaetano 10y ago
Sounds like a way to troll other organizations and competitors, imagine the conversations: -(Eng) We need to switch to this new NLP framework - (VP) Ok, why? Which one is it? - (Eng) Huh, it's called Parsey McParseface, developed by ... - (VP) WTF? Don't waste my time with jokes, go build your own - (Eng) But ... - (VP) Meeting's over.

minimaxir 10y ago

See also: spaCy, which is an open-source NLP framework that has some integration with Keras as well: https://news.ycombinator.com/item?id=13874787 https://news.ycombinator.com/item?id=13874787 ...and apparently will release a major version update today. Ouch.

binarymax 10y ago
I don't think spaCy will be hurting any time soon. When SyntaxNet was first released last year, Matthew Honnibal had a good writeup [0] of how spaCy vastly outperforms with speed while keeping reasonable accuracy: >On the time-honoured benchmark for this task, Parsey McParseface achieves over 94% accuracy, at around 600 words per second. On the same task, spaCy achieves 92.4%, at around 15,000 words per second. The extra accuracy might not sound like much, but for applications, it's likely to be pretty significant. If spaCy is able to increment the accuracy and maintain the large performance gap, it'll still be my go-to NLP framework! [0] https://explosion.ai/blog/syntaxnet-in-context https://explosion.ai/blog/syntaxnet-in-context
syllogism 10y ago
Why ouch? :). It's not like there's a zero-sum game here. It's great to see more things being released, so the ecosystem can continue to improve. I do wish SyntaxNet were a bit easier to use. A lot of people have asked for SyntaxNet as a backend for spaCy, and I'd love to be using it in a training ensemble. When I tried this last year, I had a lot of trouble getting it to work as a library. I spent days trying to pass it text in memory from Python, and it seemed like I would have to write a new C++ tensorflow op. Has anyone gotten this to work yet?
- Tpt 10y ago
  There is https://github.com/livingbio/syntaxnet_wrapper https://github.com/livingbio/syntaxnet_wrapper that does the job fairly well (I also spent days trying to be able to pass to SyntaxNet different textes without having to reload the model). Warning: installation is a bit difficult.
dharma1 10y ago

canada_dry 10y ago

Hoping this will quickly make into someone's home grown self-hosted version of Alexa. Alexa, turn the lights on in the kitchen. Alexa, turn on the kitchen light. Alexa, light up the kitchen. Should all accomplish the same task using this framework.

axiom92 10y ago
I see your point, but the tasks that you have listed (and more difficult variants) can be easily handled using rule-based systems.
- losteric 10y ago
  If I recall correctly, most of Alexa is simple rule-based systems.
buro9 10y ago
> Alexa, light up the kitchen May also be interpreted as: Alexa, set fire to the kitchen
squeaky-clean 10y ago
I've been slowly working on my own simple home "Alexa" using mostly CMUSphinx for the voice detection. Honestly my most successful methods involved the least amount of complex NLP. Just simply treating the sentence as a bag of words and looking for "on" or "off" or "change" (and their synonyms) and the presence of known smart objects works extremely well. I could say "Hey Marvin, turn on the lights and TV", or "Hey Marvin, turn the lights and TV on", or even "Hey Marvin, on make lights and TV." (It's named Marvin it after the android from The Hitchhiker's Guide, my eventual goal is to have it reply with snarky/depressed remarks). Adding 30 seconds of "memory" of the last state requested also made it seem a million times smarter and turns requests into a conversation rather than a string of commands. If it finds a mentioned smart object with no state mentioned, it assume the previous one. "Hey Marvin, turn on the lights." lights turn on "The TV too." tv turns on The downside to this approach is I would be showing it off to friends, and it could mis trigger. "Marvin turn off the lights." lights turn off "That's so cool, so it controls your TV, too?" TV turns off But it was mostly not an issue in real usage. Ultimately I've got the project on hold for now because I can't find a decent, non-commercial way of converting voice to text. I'd really rather not send my audio out to Amazon/Google/MS/IBM. Not just because of privacy, but cost and "coolness" factor (I want as much as possible processed locally and open-source). CMUSphinx's detection was mostly very bad. I couldn't even do complex NLP if I wanted because it picks up broken/garbled sentences. I currently build a "most likely" sentence by looping through sphinx's 20 best interpretations of the sentence and grabbing all the words that are likely to be commands or smart objects. I tried setting up Kaldi, but didn't get it working after a weekend and haven't tried again since. I don't really know any other options to use aside from CMUSphone, Kaldi, or a butt SaaS. I've wanted to add a text messaging UI layer to it. Maybe I'll use that as an excuse to try playing with ParseySaurus.

devy 10y ago

"Python 3 support is not available yet." [1]. It's only supported in Python 2.7, Why? [1] https://github.com/tensorflow/models/tree/master/syntaxnet https://github.com/tensorflow/models/tree/master/syntaxnet

RussianCow 10y ago
Probably because Google still mostly uses Python 2.7 internally.
- devy 10y ago
  Yikes! Not being to run on Py3k is a deal breaker for me.
  - akinalci 10y ago
    It's definitely an unfortunate situation. The community has been coalescing around Python 3 in the last couple years, but Google is obviously encumbered by all its legacy Python 2.7 code. Their SyntaxNet library still doesn't have Python 3 support a year after release. I'm wondering what their long-term plans are given 2.7 EOL in 2020.
deleted 10y ago
[deleted]
jorgemf 10y ago
Only for the models, the core works with python 3. I tweaked the models to use them in python 3, it is things like 'xrange and range'.
- dmorr 10y ago
  Check in a python 3 version of the code? The world will thank you!

jacquesm 10y ago

I've been fighting Tensorflow in the last couple of days to try an application on it, never before have I seen such a convoluted build process and a maze of dependencies. The best manual on getting tensorflow with CUDA support up and running is here: http://www.nvidia.com/object/gpu-accelerated-applications-tensorflow-installation.html http://www.nvidia.com/object/gpu-accelerated-applications-te... But it is a little bit out of date when it comes to version numbers. If you're going to try TensorBox (https://github.com/TensorBox/TensorBox https://github.com/TensorBox/TensorBox) it will get a bit harder still because of conflicts and build issues with specific versions of TensorFlow. There has to be an easier way to distribute a package. That said, all this is super interesting and Google really moved the needle by opensourcing TensorFlow and other ML packages.

jorgemf 10y ago
It is not a good idea to compile TensorFlow by your own unless you really need it (for example for TensorFlow serving). Python packages are the way to go.
- jacquesm 10y ago
  I have to because I'm trying to use TensorBox which does not play well with the regular version of tensorflow that you can get pre-compiled. See this issue: https://github.com/TensorBox/TensorBox/issues/100 https://github.com/TensorBox/TensorBox/issues/100 and https://github.com/TensorBox/TensorBox/issues/102 https://github.com/TensorBox/TensorBox/issues/102 So then we're full-circle and installing from pip which doesn't work :( sigh. Anyway, I'll get it to work, somehow.
  - jorgemf 10y ago
    Those issues are related with TensorFlow <1.0. It wasn't an stable release. Try 1.0.1.
- aseipp 10y ago
  This is a bit unfortunate, in a real sense. I mean, I already build enough software, so I'm not sad on missing out. But here's the thing: TensorFlow actually installed great on Windows and it took less than 10 minutes to get running, once I had Python3 installed, even with GPU support. Even worked awesome in VS Code, out of the box, with autocomplete in the python mode. Even a baby like me got started easily. But it's a bit disappointing to hear that the build system is something of a nightmare, if I ever wanted to contribute myself. There's always plenty of things to help with, I don't care about the cutting edge of machine learning (I'm happy to submit docs, examples, etc)... Then again, the TF people can't just nerd around on their build system, for dorks like me to maybe write some patches every once in a while. Always great to make it easier, though.

Winblows69 10y ago

Overall, do you think Google does more good than harm?

deleted 10y ago

[deleted]

tlow 10y ago

For those of us who aren't developers but maybe more aptly called "hackers" (cause we hack stuff together even though we're operating out of our league, sometimes we get stuff to work). I am wondering, is there a even higher level guide to using Tensor Flow. I am currently growing Sweet Peas in my office in enclosed containers that automanage environment, nutrition and water. I have the capaability to log a lot of data from a lot of sensors, including images. I have _no idea_ how I would even get started using Tensor Flow, but it would be cool if I could run experiments on environmental conditions and find optimal conditions for this sweet pea cultivar. Maybe I'm talking nonsense. Let me ask a more basic question, how might one log and create data for use with Tensor Flow. How might Tensor Flow be applied to robotic botanical situations?

minimaxir 10y ago
The short answer is to skip TensorFlow entirely and use/learn Keras for a high-level overview; then you can learn top-down if you need to use/look at TF code directly. Another HN thread has good tutorials for simple uses of Tensorflow: https://news.ycombinator.com/item?id=13464496 https://news.ycombinator.com/item?id=13464496 However, NNs are optimal for text/image data as they can learn the features. If your data features are already known, you don't necessarily need to use Tensorflow/Keras at all, and you'll have a easier time using conventional techniques like linear/logistic regression and xgboost.
- Houshalter 10y ago
  sklearn has this flowchart for what machine learning method to use: http://scikit-learn.org/stable/_static/ml_map.png http://scikit-learn.org/stable/_static/ml_map.png
  - minimaxir 10y ago
    The flowchart predates NNs/GBTs which are Swiss-army knives, which is another reason why using either of them is sometimes considered cheating.

camdenlock 10y ago

> and to allow neural-network architectures to be created dynamically during processing of a sentence or document. Oh lord, is this the spark that lights the google skynet powder keg

PeterisP 10y ago
Not yet, you'd need to apply the same approach to agentive (decisions on how to act) problems as opposed to classification tasks; then you'd have the spark that lights the google skynet powder keg.

jakekovoor 10y ago

This is definitely a game changer! It's a very interesting research carried out by Google's research team and I believe this will be especially beneficial for future speech translation algorithms that would bring us a whole new, fresh experience with the way we converse with Alexa, Google Home, Siri, and many more. If you need to install TensorFlow onto your Windows 10 computer then here's a great guide which I have followed quiet a few times. :) http://saintlad.com/install-tensorflow-on-windows/ http://saintlad.com/install-tensorflow-on-windows/

dang 10y ago

We changed the title from "Google open-sources Tensorflow-based framework for NLP", which appears misleading, given that it happened last May: https://news.ycombinator.com/item?id=11686029 https://news.ycombinator.com/item?id=11686029. On HN the idea is to rewrite titles only to make them less misleading (or less baity). Please see https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html.

liviosoares 10y ago
dang, sorry if this seemed misleading. In my humble opinion, the blog title does not do full justice to the new release, primarily since it carries a new framework within SyntaxNet: https://github.com/tensorflow/models/blob/master/syntaxnet/g3doc/DRAGNN.md https://github.com/tensorflow/models/blob/master/syntaxnet/g... This new DRAGNN framework is what I thought the folks here would want to know. Perhaps I should have linked to the github page, rather than the blog announcement.
- dang 10y ago
  Ah, I see. Probably a post pointing to that framework would have been a better idea. It never fails to surprise me, but discussion tends to be directed almost entirely by what's in a submission title. For the same reason, it probably doesn't make sense to change the current thread to point to that Github page now, since that would orphan the existing discussion.

coding123 10y ago

I've been rummaging through the docs on DRAGONN but can't seem to find proper installation/run instructions. There is a Google Cloud installation, but I want to just run on my laptop for now against the pre-trained files. Can't seem to get started with DRAGONN.

coding123 10y ago
Thank you thank you Ivan Bogatyy for fixing the docker run instructions. I think it was missing the docker image name before! :)

David23 10y ago

I have tried to run Syntaxnet as library but I found it very difficult (lot of dependencies).