AI News, From machine learning to machine reasoning
- On Wednesday, June 6, 2018
- By Read More
From machine learning to machine reasoning
We have already demonstrated the possibility to learn salient word embeddings using an essentially non supervised task (Collobert et al.
proven way to create a rich algebraic system is to define operations that take their inputs in a certain space and produce outputs in the same space.
The association module is a trainable function that takes two vectors in the representation space and produces a single vector in the same space, which is expected to represent the association of the two input vectors.
Applying the saliency scoring module R to all intermediate results and summing all the resulting scores yields a global score measuring how meaningful is a particular way to bracket a sentence.
The supervised training approach also provides a more objective way to assess the results since one can compare the bracketing performance of the system with that of established parsers.
There is therefore much work left to accomplish, including (i) robustly addressing all the numerical aspects of the training procedure, (ii) seamlessly training using both supervised and unsupervised corpora, (iii) assessing the value of the sentence fragment representations using well known NLP benchmarks, and (iv) finding a better way to navigate these sentence fragment representations.
This can be achieved by augmenting the earlier loss function (Fig. 8) with terms that apply the dissociation module to each presumed meaningful intermediate representation and measure how close its outputs are from the inputs of the corresponding association module.
The association and dissociation modules are similar to the primitives cons, car and cdr, which are the elementary operations to navigate lists and trees in the Lisp computer programming languages.
We can then parse an initial sentence and construct its representation, convert the representation into the representation of the same sentence in the past tense, and use the dissociation module to reconstruct the converted sentence.
A number of state-of-the-art systems for scene categorization and object recognition use a combination of strong local features, such as SIFT or HOG features, consolidated along a pyramidal structure (e.g., Ponce et al.
Since viewpoint changes can also reveal or hide entire objects, such modules could conceivably provide a tool for constructing a vision system that implements object permanence (Piaget 1937).
Using pre-trained ELMo representations
Pre-trained contextual representations from large scale bidirectional language
If you need to include ELMo at multiple layers in a task model or you have other advanced use cases, you will need to create ELMo vectors programatically. This
is easily done with the Elmo class (API doc), which provides a mechanism to compute the weighted ELMo representations (Equation (1) in the paper).
First, modify the text_field_embedder section by adding an elmo section as follows: Second, add an elmo section to the dataset_reader to convert raw text to ELMo character id sequences in addition to GloVe ids: Third, modify the input dimension (input_size) to the stacked LSTM encoder. The
baseline model uses a 200 dimensional input (100 dimensional GloVe embedding with 100 dimensional feature specifying the predicate location). ELMo
Finally, we have found that in some cases including pre-trained GloVe or other word vectors in addition to ELMo provides little to no improvement over just using ELMo and slows down training.
tokens, it will reset the internal states to its own internal representation of sentence break when seeing these tokens.
The Keras Blog
In this tutorial, we will walk you through the process of solving a text classification problem using pre-trained word embeddings and a convolutional neural network.
The task we will try to solve will be to classify posts coming from 20 different newsgroup, into their original 20 categories --the infamous '20 Newsgroup dataset'.
Here are a few sample categories: Here's how we will solve the classification problem: First, we will simply iterate over the folders in which our text samples are stored, and format them into a list of samples.
We will also prepare at the same time a list of class indices matching the samples: Then we can format our text samples and labels into tensors that can be fed into a neural network.
Next, we compute an index mapping words to known embeddings, by parsing the data dump of pre-trained embeddings: At this point we can leverage our embedding_index dictionary and our word_index to compute our embedding matrix: We load this embedding matrix into an Embedding layer.
These input sequences should be padded so that they all have the same length in a batch of input data (although an Embedding layer is capable of processing sequence of heterogenous length, if you don't pass an explicit input_length argument to the layer).
Finally we can then build a small 1D convnet to solve our classification problem: This model reaches 95% classification accuracy on the validation set after only 2 epochs.
In general, using pre-trained embeddings is relevant for natural processing tasks were little training data is available (functionally the embeddings act as an injection of outside information which might prove useful for your model).
Deep Learning, Where Are You Going?
발표자: 조경현 (NYU 교수) Kyunghyun Cho is an assistant professor of computer science and data science at New York University.
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