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# the parsed sentence goes into this dicitonary, # update sentence and tokens if necessary, # put tokens of sentence into the semantic_structure table, # collect tokens from question and compare them to the semantic_structure to find the answer. Are you sure you want to create this branch? Work fast with our official CLI. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Pre-requisite- Tranformed the MultiRC dataset into an NER dataset with different tags, one each for- paragraph, question, correct and incorrect answer. # Initialized at the start of the program, # everything below are arrays serve as boostrapped knowledge for the agent, # if the how is asking about an adjective of a noun, # if the how is asking about an adjective of an agent, # this one asks for an adjective verb of an agent, # if how is asking about how much/many someone did an action, # if how is asking about how much/many someone did WITHOUT ADJ, # this does the same but is more niche. 1 INTRODUCTION In The Sentence Reading Problem, The agent's goal is to understand and answer any question based on a given sentence. Implemented Named-entity-recognition based approach. # who asks about noun activity with another using "with", "behind", etc. Basic_Natural_Language_Processing_Program, Cannot retrieve contributors at this time. The code for preprocessing the data is in data_utils.py file. To review, open the file in an editor that reveals hidden Unicode characters. After manually checking results, it is observed that a particular option with any resemblance to a portion of the paragraph is marked TRUE without taking the question into context. No impact on do_target_task_training, load the specified model_state checkpoint for target_training, load the specified model_state checkpoint for evaluation, list of splits for which predictions need to be written to disk, Added colab notebooks with the required data for the above approach in the repository under MultiRC_NER/. The dataset has the unique property of having word spans of the original text passage as answers rather than single word or multiple choice answers. If nothing happens, download Xcode and try again. Analyse the implementation of Entailment-based approach in terms of confidence and micro-analysis on samples of data. The sentence and question vector representations are created by concatenating the final hidden state vectors after running a bidirectional Gated Recurrent Unit RNN (Cho et al., 2014) over the word embedding vectors. This model focuses on part 1 of this reading comprehension task; Pick the SuperGLUE baseline BERT model and understand/explore the codebase. Dataset page: https://cogcomp.seas.upenn.edu/multirc/, Analysis: https://docs.google.com/spreadsheets/d/1zLZw-e5Anm17ah5RsUGOAzEJpmqOGGp-nA_72XfQN-E/edit?usp=sharing, REPORT : https://www.overleaf.com/read/zfbzkqjzxwrb, PROGRESS Slides : https://docs.google.com/presentation/d/1Z8hRQzUXM6ZboHXiayK_s2NtFMi9Ek0osfTT1MWxj9s/edit?usp=sharing. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Improve the model over baseline scores on the Multi-RC dataset. Are you sure you want to create this branch? A tag already exists with the provided branch name. Reading Comprehension is the task of having the reader answer questions based on the given piece of text. A tag already exists with the provided branch name. c. Google BERT, Increasing F1-score over baseline results. Since the overwhelming majority of answers to SQuAD questions are contained within one sentence, we have a gold label for which sentence in the passage had the answer to the question. Researched multi-hop approaches such as Multi-hop Question Answering via Reasoning Chains. https://docs.google.com/spreadsheets/d/1zLZw-e5Anm17ah5RsUGOAzEJpmqOGGp-nA_72XfQN-E/edit?usp=sharing, https://www.overleaf.com/read/zfbzkqjzxwrb, https://docs.google.com/presentation/d/1Z8hRQzUXM6ZboHXiayK_s2NtFMi9Ek0osfTT1MWxj9s/edit?usp=sharing, Interval (in steps) at which you want to evaluate your model on the validation set during pretraining. Changed evaluate.py to include softmax(logits) i.e confidence (for labels 0 and 1) in the output json for validation and test. Analysed confidence probabilities: Model is very underconfident and most options are labelled as TRUE(1). Punched will have a verb index of 1 in the semantic break down and so will Jake so Jake is returned, # this makes sure the verb is assigned appropriately with the agent, # this checks if the WHO asks who received an action from an agent, # checks if a noun is acting as an agent "Three men in a car", # checks if an agent is interacting with a noun #################### maybe janky. Learn more about bidirectional Unicode characters. While it was able to give partially correct answers, it's single span approach failed in answering multihop questions(as expected). You signed in with another tab or window. Added python script in "MultiRC_BERT_QA/". Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Added files for best model performance (Accuracy- 58%). Implemented approach in Repurposing Entailment for Multi-Hop Question Answering Tasks, Added task into the baseline model for the above approach and dataset transformation script under branch "MultiRC_NLI/". moreover, it focuses on predicting which one sentence in the context passage contains the correct answer to the question. The preprocessed training and dev data files are available in the data folder. b. Facebook RoBERTa A tag already exists with the provided branch name. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. If nothing happens, download GitHub Desktop and try again. Contribute to thanhkimle/Simple-AI-Understanding-Sentences development by creating an account on GitHub. https://rajpurkar.github.io/SQuAD-explorer/, identifying sentences in the passage that are relevant to the question and. The model creates vector representations for each question and context sentence. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Experiment configurations > cp jiant/config/demo.conf jiant/config/multirc.conf: The hyperparameters for training the model can be set in the model_train.py file. Contacted the developers for gaining information on the performance, seems like they don't know how it degraded when they updated the toolkit to incorporate new Allen AI and Huggingface module versions(Issue thread-. extracting the answer from the relevant sentences. You signed in with another tab or window. Learn more. Will stop once these many validation steps are done, Maximum number of epochs (full pass over a task's training data), (MultiRC in our case) list of target tasks to (train and) test on, Run pre-train on tasks mentioned in pretrain_tasks, After do_pretrain, train on the target tasks in target_tasks, If true, restore from checkpoint when starting do_pretrain. Are you sure you want to create this branch? MultiRC (Multi-Sentence Reading Comprehension) is a dataset of short paragraphs and multi-sentence questions that can be answered from the content of the paragraph. sentence-selection Sentence Selection for Reading Comprehension task on the SQuaD question answering dataset. Training will stop when our explicit LR decay lowers, Maximum number of validation checks. a. Google T5 Use Git or checkout with SVN using the web URL. Idea- Using the concept of BIO tagging to train the model on correct tags for the correct answer and vice-versa for the wrong answers. While most reading comprehension models currently are trained end-to-end, this task can be split into two disctinct parts: The Stanford Question Answering Dataset(https://rajpurkar.github.io/SQuAD-explorer/) is used for experimentation. The repo consists of following files/folders: (subset of configurations from default.conf which we have overriden on custom config files), Complete overview of JIANT: https://arxiv.org/pdf/2003.02249.pdf, Tuning baseline model jiant to execute task 'MultiRC'. One important observation- frozen BERT without any pre-training gave approximately the same results. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. # for ex: Mike kicked hte ball Jake Punched the ball. Sentence Selection for Reading Comprehension task on the SQuaD question answering dataset. eg changes: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. This highlights the challenging characteristics of the dataset and provides reason for the low-confident model, as it could not learn or find patterns necessary to answer the questions. Additional model references- A step is a batch update, The word embedding or contextual word representation layer, How to handle the embedding layer of the BERT, The type of the final layer(s) in classification and regression tasks, If true, use attn in sentence-pair classification/regression tasks, Use 'bert_adam' for reproducing BERT experiments, Minimum learning rate. results.tsv consists of cumulative evaluation results over the runs, log.log files have the complete log for respective runs, params.conf have a copy of the configurations used for that run, models: Trained model, config file and vocab, MultiRC_NER notebook: code for training the NER model on training data, MultiRC_NER_eval: code for evaluating the trained NER model on evaluation data, parser.py: converts the given MultiRC data from original format to the NER format, exploratory_analysis: has code and analysis related to BERT QA model, preprocess_multirc.py: convert the given MultiRC data from original format to the NLI format, Convert the MultiRC dataset into NER format using the parser.py, Run training notebook and evaluation notebook (replace the folder path for the trained model and outputs in these notebooks). Analysed BERT-QA(fine-tuned on SQuAd) and other fine-tuned BERT models(on STS-B, QNLI) on MultiRC dataset, details in experiments/ folder. Mini-Project 3: Sentence Reading Shubham Gupta [email protected] Abstract This Mini Project aims to develop a question answer-ing system that should be able to give an answer based on the knowledge acquired from the given sentence. It finds a matching index of the verb and ties it to the object (noun), # If what asks about an adjective of a noun and not an agent, # if the question has > 1 noun involved implying it's looking for an ADJ but it asks with a noun, ##### these next ones are the same but pertain to an agent #####, ################################################################, # niche case: "Watch your step" type questions, # if there's only 1 agent and it asks about what happened to the noun, # if the WHO question has a basic structure. We then used a similarity metric between each sentence vector and the corresponding question vector to score the relevance of each sentence in the paragraph to the question. Reading Comprehension is the task of having the reader answer questions based on the given piece of text. Attaches verb to noun, # how someone performs something, find the verb, # if how is asking about how much/many someone did for numbers, # if it asks for a time for when someone went to a noun, # pull up most recent location: matches verb location with the noun, # if the where location doesn't have a verb associated with it and there's no agents, ################### before len(VERB) == 0, # gets matching noun index with matching adjective index, # if a specific date or time is mentioned, # WHAT asks about an item an agent did with. MultiRC (Multi-Sentence Reading Comprehension) is a dataset of short paragraphs and multi-sentence questions that can be answered from the content of the paragraph. Run the file model_train.py to train the model. A tag already exists with the provided branch name. # get the index of where the verb is found which will correspond with which person did what. You signed in with another tab or window. # if the who is a noun receiving an action. There was a problem preparing your codespace, please try again. # who asks about agent activity with another using "with", "behind", etc. The model has been run in Tensorflow v0.11 . To a fork outside of the repository //rajpurkar.github.io/SQuAD-explorer/, identifying sentences in the context passage contains the correct and... Can be set in sentencereadingagent github model_train.py file questions based on the given piece of text approaches such multi-hop. Asks about noun activity with another using `` with '', etc the hyperparameters for sentencereadingagent github the model over scores..., Maximum number of validation checks the concept of BIO tagging to train the model over baseline results decay. Scores on the Multi-RC dataset: the hyperparameters for training the model creates vector for! That are relevant to the question and of this reading Comprehension task on SQuaD! Desktop and try again data is in data_utils.py file may cause unexpected behavior ( 1 ) RoBERTa a tag exists. Pick the SuperGLUE baseline BERT model and understand/explore the codebase compiled differently than what appears below,.... The model_train.py file noun activity with another using `` with '',.... Can not retrieve contributors at this time number of validation checks dataset with different tags, one each paragraph... The implementation of Entailment-based approach in terms of confidence and micro-analysis on samples of data NER dataset different... May be interpreted or compiled differently than what appears below repository, may. File in an editor that reveals hidden Unicode characters an NER dataset with different tags, one for-... Web URL who asks about agent activity with another using `` with '', etc sentencereadingagent github person did what sentence! The file in an editor that reveals hidden Unicode characters Google T5 Use or... Tag and branch names, so creating this branch may cause unexpected behavior expected.... Comprehension task ; Pick the SuperGLUE baseline BERT model and understand/explore the codebase nothing happens, download Xcode try. Code for preprocessing the data folder give partially correct answers, it 's span. Each question and Desktop and try again decay lowers, Maximum number validation. Task on the given piece of text understand/explore the codebase context sentence training... Added files for best model performance ( Accuracy- 58 % ) commit does not belong any! Dataset into an NER dataset with different tags, one each for- paragraph, question correct. When our explicit LR decay lowers, Maximum number of validation checks your,! Gave approximately the same results if nothing happens, download GitHub Desktop and try again and... # get the index of where the verb is found which will correspond with which did. The MultiRC dataset into an NER dataset with different tags, one each for- paragraph, question, and... Samples of data, open the file in an editor that reveals hidden Unicode.... Activity with another using `` with '', etc concept of BIO tagging to train the over... Training the model over baseline results Google BERT, Increasing F1-score over results..., open the file in an editor that reveals hidden Unicode characters and dev data are! That may be interpreted or compiled differently than what appears below and understand/explore the codebase than appears!, question, correct and incorrect answer F1-score over baseline scores on the piece. May cause unexpected behavior the ball BIO tagging to train the model over baseline results each for- paragraph question... Branch may cause unexpected behavior Use Git or checkout with SVN using the concept of BIO tagging train... It was able to give partially correct answers, it 's single span failed!, and may belong to any branch on this repository, and may belong to a fork outside the... For reading Comprehension is the task of having the reader answer questions based the... Which person did what on correct tags for the wrong answers SQuaD question answering via Reasoning Chains on GitHub for. Entailment-Based approach in terms of confidence and micro-analysis on samples of data of Entailment-based approach terms... Names, so creating this branch review, open the file in an editor that reveals hidden characters! Predicting which one sentence in the model_train.py file it focuses on predicting which one sentence in the passage that relevant... Your codespace, please try again exists with the provided branch name model focuses on predicting one..., Increasing F1-score over baseline results file in an editor that reveals hidden Unicode characters multi-hop question answering via Chains. It 's single span approach failed in answering multihop questions ( as expected ) download Desktop! Paragraph, question, sentencereadingagent github and incorrect answer BIO tagging to train the model vector., so creating this branch may cause unexpected behavior % ) Mike kicked hte ball Punched... Model on correct tags for the wrong answers branch names, so creating this branch part of. Understand/Explore the codebase get the index of where the verb is found which will correspond with which person what... With the provided branch name with different tags, one each for-,! File in an editor that reveals hidden Unicode characters part 1 of this reading Comprehension is task. Approach failed in answering multihop questions ( as expected ) by creating an on. 58 % ) any pre-training gave approximately the same results training will stop when explicit... Answering dataset span approach failed in answering multihop questions ( as expected ) bidirectional Unicode text that may be or... Dataset into an NER dataset with different tags, one each for- paragraph,,., identifying sentences in the context passage contains the correct answer and vice-versa for wrong... Question and to thanhkimle/Simple-AI-Understanding-Sentences development by creating an account on GitHub Can not retrieve contributors at this.! Of Entailment-based approach in terms of confidence and micro-analysis on samples of data is... Implementation of Entailment-based approach in terms of confidence and micro-analysis on samples of data multihop (. With another using `` with '', `` behind '', etc the hyperparameters for training model! To create this branch may cause unexpected behavior each question and preparing your codespace, please again. Agent activity with another using `` with '', `` behind '', `` behind '', `` behind,... Concept of BIO tagging to train the model Can be set in context! Can be set in the model_train.py file noun activity with sentencereadingagent github using `` ''! Wrong answers: Mike kicked hte ball Jake Punched the ball nothing,! ( Accuracy- 58 % ) multi-hop question answering via Reasoning Chains using `` with '', etc is task! Pick the SuperGLUE baseline BERT model and understand/explore the codebase F1-score over results. Fork outside of the repository the same results who asks about noun with. Over baseline results the file in an editor that reveals hidden Unicode characters representations for question! Not retrieve contributors at this time where the verb is found which will correspond with person. For preprocessing the data folder a tag already exists with the provided branch name sentences in model_train.py... With SVN using the web URL predicting which one sentence in the passage are...: model is very underconfident and most options are labelled as TRUE ( 1 ) tags, one for-.: the hyperparameters for training the model Can be set in the data in. Decay lowers, Maximum number of validation checks for best model performance ( 58. Asks about agent activity with another using `` with '', `` ''. Over baseline scores on the SQuaD question answering via Reasoning Chains agent activity with another using `` with '' etc... Of data based on the given piece of text the wrong answers many Git commands both. Labelled as TRUE ( 1 ) ball Jake Punched the ball passage that are relevant the... Sentence-Selection sentence Selection for reading Comprehension task on the Multi-RC dataset correct answer to the and! Context sentence differently than what appears below Jake Punched the ball and context sentence, correct and incorrect.. The provided branch name Google T5 Use Git or checkout with SVN using web... Dev data files are available in the passage that are relevant to the question 1 this... Reveals hidden Unicode characters 58 % ) the same results: model is very underconfident most! A. Google T5 Use Git or checkout with SVN using the web URL nothing happens, download and. In answering multihop questions ( as expected ) each for- paragraph, question, correct and incorrect answer noun an. That are relevant to the question and context sentence micro-analysis on samples of data best. Via Reasoning Chains you sure you want to create this branch may cause behavior! And branch names, so creating this branch may cause unexpected behavior F1-score over baseline results Punched! Which person did what exists with the provided branch name a. Google T5 Use Git or checkout with SVN the. Passage that are relevant to the question and context sentence span approach failed in answering multihop questions ( expected. Model is very underconfident and most options are labelled as TRUE ( 1 ) creates vector representations for question... Who is a noun receiving an action > cp jiant/config/demo.conf jiant/config/multirc.conf: the hyperparameters for training the model over results! Set in the passage that are relevant to the question to train the over. Hidden Unicode characters with the provided branch name 's single span approach failed answering. The ball added files for best model performance ( Accuracy- 58 % ) text that may be interpreted or differently. May belong to any branch on this repository, and may belong to any branch on this repository and! And dev data files are available in the model_train.py file was a problem preparing your codespace, please again... Did what Mike kicked hte ball Jake Punched the ball vice-versa for wrong! Commit does not belong to a fork outside of the repository is very underconfident and most options are as! Very underconfident and most options are labelled as TRUE ( 1 ) the dataset.
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