Freda Shi's Projects
[ACL 2021] Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment
An open source implementation of paper ``Quantifying the visual concreteness of words and topics in multimodal datasets'' (Hessel et al., NAACL 2018)
Deep Inside-Outside Recursive Autoencoder
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Personal python toolbox.
An open repository of jsPsych plugins and extensions, without any official support
code for "Natural Language to Code Translation with Execution"
NL-Augmenter 🦎 → 🐍 A Collaborative Repository of Natural Language Transformations
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
NLTK Source
NLTK Data
Slides of my lectures on Olympiad in Informatics.
Code for the paper "Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks"
A collection of state-of-the-art syntactic parsing models based on Biaffine Parser.
Parsing Reading Predict Network
Official codes for the paper "Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speech"
Code for "Does syntax need to grow on trees? Sources of inductive bias in sequence to sequence networks"
High-accuracy NLP parser with models for 11 languages.
Obtain Word Alignments using Pretrained Language Models (e.g., mBERT)
structured intersection-over-union ratio for evaluation of (speech) constituency parsing.
An open-source implementation of the paper ``A Structured Self-Attentive Sentence Embedding'' (Lin et al., ICLR 2017).
[ACL 2021 Findings] Subtree/subsequence substitution as data augmentation
NAACL 2021: Are NLP Models really able to Solve Simple Math Word Problems?
An efficient toolkit for syntactic evaluation (Marvin and Linzen, EMNLP 2018; Gulordava et al., NAACL 2018).
[EMNLP 2018] On Tree-Based Neural Sentence Modeling.
A framework to learn cross-lingual word embedding mappings
[ACL 2019] Visually Grounded Neural Syntax Acquisition
[COLING 2018] Learning Visually-Grounded Semantics from Contrastive Adversarial Samples.