1 INTRODUCTION In The Sentence Reading Problem, The agent's goal is to understand and answer any question based on a given sentence. 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. 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. This model focuses on part 1 of this reading comprehension task; moreover, it focuses on predicting which one sentence in the context passage contains the correct answer to the question. Analyse the implementation of Entailment-based approach in terms of confidence and micro-analysis on samples of data. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. You signed in with another tab or window. Learn more about bidirectional 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. Contribute to thanhkimle/Simple-AI-Understanding-Sentences development by creating an account on GitHub. Use Git or checkout with SVN using the web URL. One important observation- frozen BERT without any pre-training gave approximately the same results. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. eg changes: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Additional model references- 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. Are you sure you want to create this branch? Sentence Selection for Reading Comprehension task on the SQuaD question answering dataset. A tag already exists with the provided branch name. 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. 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. b. Facebook RoBERTa 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. extracting the answer from the relevant sentences. While it was able to give partially correct answers, it's single span approach failed in answering multihop questions(as expected). If nothing happens, download Xcode and try again. c. Google BERT, Increasing F1-score over baseline results. sentence-selection Sentence Selection for Reading Comprehension task on the SQuaD question answering dataset. 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. a. Google T5 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. You signed in with another tab or window. Pre-requisite- Tranformed the MultiRC dataset into an NER dataset with different tags, one each for- paragraph, question, correct and incorrect answer. A tag already exists with the provided branch name. Pick the SuperGLUE baseline BERT model and understand/explore the codebase. Researched multi-hop approaches such as Multi-hop Question Answering via Reasoning Chains. A tag already exists with the provided branch name. 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-. A tag already exists with the provided branch name. 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. 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'. There was a problem preparing your codespace, please try again. 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. 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. # who asks about noun activity with another using "with", "behind", etc. https://rajpurkar.github.io/SQuAD-explorer/, identifying sentences in the passage that are relevant to the question and. Added files for best model performance (Accuracy- 58%). Learn more. Are you sure you want to create this branch? 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/. Analysed BERT-QA(fine-tuned on SQuAd) and other fine-tuned BERT models(on STS-B, QNLI) on MultiRC dataset, details in experiments/ folder. Changed evaluate.py to include softmax(logits) i.e confidence (for labels 0 and 1) in the output json for validation and test. # 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. 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). # if the who is a noun receiving an action. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. Improve the model over baseline scores on the Multi-RC dataset. # for ex: Mike kicked hte ball Jake Punched the ball. # 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Experiment configurations > cp jiant/config/demo.conf jiant/config/multirc.conf: 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. The preprocessed training and dev data files are available in the data 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. Added python script in "MultiRC_BERT_QA/". The model has been run in Tensorflow v0.11 . Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Are you sure you want to create this branch? Training will stop when our explicit LR decay lowers, Maximum number of validation checks. 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. # who asks about agent activity with another using "with", "behind", etc. Basic_Natural_Language_Processing_Program, Cannot retrieve contributors at this time. 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. Work fast with our official CLI. The model creates vector representations for each question and context sentence. 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. If nothing happens, download GitHub Desktop and try again. You signed in with another tab or window. 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/". # get the index of where the verb is found which will correspond with which person did what. The hyperparameters for training the model can be set in the model_train.py file. 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. Analysed confidence probabilities: Model is very underconfident and most options are labelled as TRUE(1). 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. Implemented Named-entity-recognition based approach. Reading Comprehension is the task of having the reader answer questions based on the given piece of text. Reading Comprehension is the task of having the reader answer questions based on the given piece of text. Run the file model_train.py to train the model.
Legendary Tale An Adventure Crossword Clue, Glacial Deposit Types, Adventist Health White Memorial Rn Residency Program, Bradford Doctors Accepting New Patients, Unit Of Resistance Crossword Clue 3 Letters, Tensorboard Bad Interpreter: No Such File Or Directory, External Dvd Player For Windows 11, Jacques Torres Chocolate Locations,