update README

Author: jklafka

.DS_Store

@@ -1,15 +1,15 @@
 # context-probes
-What is the "context" in a contextual word vector? We investigate what vectors from popular embedders such as BERT and ELMo, along with a non-contextual GLoVe baseline, encode about their contexts.
+What is the "context" in a contextual word vector? We investigate what vectors from popular encoders such as BERT and ELMo, along with a non-contextual GLoVe baseline, encode about their contexts.
 
 ## Folders
-*targeted_tasks*: This folder holds BERT, ELMo, GPT and GLoVe embedders, along with code for constructing the input to these embedders.
+*targeted_tasks*: This folder holds BERT, ELMo, GPT and GLoVe encoders, along with code for constructing the input to these encoders.
 
-*classifiers*: The neural network learners are stored here. _pytorch_classifier.py_ is the main file of interest and has code for a single layer PyTorch perceptron classifier and a three-layer PyTorch multi-layer perceptron classifier.
+*classifiers*: The neural network learners are stored here. _pytorch_classifier.py_ is the main file of interest and has code for a single-layer neural network classifier and a three-layer neural network classifier. Both classifiers are simple multi-layer perceptron (MLP) architectures to increase the interpretability of results and decrease the chance of overfitting.
 
-*results*: Holds .csv files with the results of the probing tasks. The files are in tidy data format: each line has the name of the embedder, the architecture (i.e. size) of the classifier network, the index of the word in our five-word sentences for which the contextualized embedding was constructed, and the performance on the test set.
+*results*: Holds .csv files with the results of the probing tasks. The files are in tidy data format: each line has the name of the encoder, the architecture (i.e. size) of the classifier network, the index of the word in our five-word sentences for which the contextualized embedding was constructed, and the performance on the test set.
 
-*data*: The version of this folder on the repository holds the final versions of the stimuli we use as input to the embedders in our experiments. Running _data-construction.py_ writes data to this folder. This "data" is the input to the embedders.
+*data*: The version of this folder on the repository holds the final versions of the stimuli we use as input to the encoders in our experiments. Running _data-construction.py_ writes data to this folder. This "data" is the input to the encoders.
 
-*stimuli*: Contains the ingredients for the data: the nouns and verbs annotated with positive and negative labels for each of the targeted tasks. _data-construction.py_ uses these ingredients to make the input to the embedders.
+*stimuli*: Contains the ingredients for the data: the nouns and verbs annotated with positive and negative labels for each of the targeted tasks. _data-construction.py_ uses these ingredients to make the input to the encoders.
 
-*word_content*: Holds the embedders for the word identity probing tasks.
+*word_content*: Holds the encoders for the word identity probing tasks.