Programming Projects
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1 Programming Projects Benjamin Roth, Nina Poerner, Anne Beyer Centrum für Informations- und Sprachverarbeitung Ludwig-Maximilian-Universität München Benjamin Roth, Nina Poerner, Anne Beyer (CIS) Programming Projects 1 / 12
2 Common-sense Reasoning I Given: Pre-trained embedding vectors for syntactic dependency paths between entities. COUNTRY-annexes-COUNTRY Task: Predict whether one dependency path implies another dependency path We have manually annotated data for training/testing (4000 instances). Example: COUNTRY-annexes-COUNTRY COUNTRY-takes-control-over-COUNTRY (TRUE) COUNTRY-annexes-COUNTRY COUNTRY-neighbors-COUNTRY (FALSE) Possible design choices: Concatenate vectors, then predict Elementwise multiplication, then predict Hidden layer before prediction Regularization... Benjamin Roth, Nina Poerner, Anne Beyer (CIS) Programming Projects 2 / 12
3 Common-sense Reasoning II Given: Tuples (Entity-Entity, dependency-path) Task: Learn embedding vectors for syntactic dependency paths between entities. We have data and evaluation scripts to check how good the vectors are the same as word2vec! Entity-Entity context word dependency-path target word (or vice versa) Possible design choices: experiment with different negative sampling strategies, e.g.: (Russia-Crimea, COUNTRY-annexes-COUNTRY, TRUE) (Russia-Crimea, PERSON-married-to-PERSON, FALSE)... Benjamin Roth, Nina Poerner, Anne Beyer (CIS) Programming Projects 3 / 12
4 Antonymy Detection Given: Pairs of Words (from Wiktionary), which are either synonyms, antonyms, or unrelated Task: Use pre-trained word-vectors, and predict which pairs are synonyms, which are antonyms, or unrelated. Possible design choices: Word2Vec, GloVe, FastText,... Concatenation, element-wise multiplication hidden-layer(s) qualitative analysis: confusion matrix, which type of words are easy/hard... Benjamin Roth, Nina Poerner, Anne Beyer (CIS) Programming Projects 4 / 12
5 Relation Argument Extraction Given: Sentence, query, relation type Task: Does the sentence contain an answer for the query and relation? I have code that predicts the answer in three different ways. ( Open-Type Relation Argument Extraction ) How does a model perform that combines the methods for answer extraction? Benjamin Roth, Nina Poerner, Anne Beyer (CIS) Programming Projects 5 / 12
6 Relation Prediction Given: Sentence with marked entity pairs Task: Predict which of a fixed set of relations (or no relation) holds between entity pair? Use existing noisy, automatically labelled data for training. Use Stanford model for prediction (PyTorch). Design choices: Number/weight of negative instances during training Learning rate number of updates comparison to feature-based SVM classifier Access to GPU necessary Benjamin Roth, Nina Poerner, Anne Beyer (CIS) Programming Projects 6 / 12
7 FOFE Character-based Encodings FOFE ( Character-based word vectors Every word is represented by a weighted sum of one-hot character vectors. Weights decay exponentially from start/end (1 Parameter) Task: Implement FOFE as a deterministic word embedding method: Input: index tensor of size (batch size x sent length x word length) Output: Word embeddings of size (batch size x sent length x char encoding) Evaluate on Tagging task from ATIS/tagging sheet (or other tagging dataset, e.g. POS) Task variants: Pytorch module or Keras Layer Make Parameter learnable Combine/compare with standard embedding layer Benjamin Roth, Nina Poerner, Anne Beyer (CIS) Programming Projects 7 / 12
8 FOFE Character-based Encodings FOFE ( Character-based word vectors Every word is represented by a weighted sum of one-hot character vectors. Weights decay exponentially from start/end (1 Parameter) Task: Implement FOFE as a deterministic word embedding method: Input: index tensor of size (batch size x sent length x word length) Output: Word embeddings of size (batch size x sent length x char encoding) Evaluate on Tagging task from ATIS/tagging sheet (or other tagging dataset, e.g. POS) Task variants: Pytorch module or Keras Layer Make Parameter learnable Combine/compare with standard embedding layer Benjamin Roth, Nina Poerner, Anne Beyer (CIS) Programming Projects 8 / 12
9 Detecting insincere questions on Quora Problem: Trolls use Q&A forums to ask questions that are purely rhetorical. Their goal is not to get a genuine answer, but to make a point/trigger a reaction. Task: Binary intent classification into sincere/insincere Why is Jinping the biggest serial religion murderer and rapist leader of the world after Mao? insincere How do sex workers handle and secure their cash earnings? sincere quora-insincere-questions-classification Task variants: Implement and test at least 2-3 architectures (e.g., CNN, LSTM, Self-Attention, etc.) Implement the search over at least 4-6 hyperparameters (e.g., regularization weights, dropout probability, optimizer, learning rate, etc.). The search should be intelligent and efficient (not brute force) Identify and incorporate pre-trained resources. Suggestion: pre-trained English FastText, BERT, ELMo (see e.g. towardsdatascience.com/ elmo-embeddings-in-keras-with-tensorflow-hub-7eb6f ) Benjamin Roth, Nina Poerner, Anne Beyer (CIS) Programming Projects 9 / 12
10 Cross-lingual misogyny detection Training Data: 4000 English tweets labelled misogynous or neutal Test Data: 4000 Italian tweets labelled the same way Task 1: Implement and compare at least 2-3 architectures that read a tweet and classify it as 1 (misogynous) or 0 (neutral). Initialize your embedding layers with pre-trained cross-lingual embeddings, e.g. MUSE ( Train on one language, test on other. Task 2: Create a cross-lingual word-embedding space, and use it for misogyny detection Least squares error linear mapping (supervised, hint: scipy.linalg.lstsq) Orthogonal mapping (supervised, hint: scipy.linalg.orthogonal procrustes) VECMAP (unsupervised) Benjamin Roth, Nina Poerner, Anne Beyer (CIS) Programming Projects 10 / 12
11 Domain Adaptation: Irony Detection Given: One larger data set (tweets, SemEval-2018 Task 3), one smaller data set (reddit comments, Kaggle) for irony detection. Task: Predict whether comments are ironic (e.g. LSTM+logistic regression). How can one domain (large data set) help prediction on another domain (smaller dataset)? Compare different settings: Add data together, give different weights to instances from data A vs. data B. Pretrain with data A, continue training with data B. Train model for data A. Train model for data B, constrain it to be similar to model A (possible stand-alone topic). Effect of Dropout/SpatialDropout1D: Make model more robust by removing words from the input during training. Effect of pre-trained word embeddings. Benjamin Roth, Nina Poerner, Anne Beyer (CIS) Programming Projects 11 / 12
12 Projects proposed by participants (discussed with me in advance) Attention for text generation from structured meaning representations (Ziad Elsayes) Poincare Embeddings for Food Corpus (Sameh Metias) Benjamin Roth, Nina Poerner, Anne Beyer (CIS) Programming Projects 12 / 12
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