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Word Embedding Character Embedding Subword Embeddling Tokenization
General Language Understanding Evaluation (GLUE)
Natural Language Generation (NLG) Natural Language Generation, as defined by Artificial Intelligence: Natural Language Processing Fundamentals, is the “process of producing meaningful phrases and sentences in the form of natural language.” In its essence, it automatically generates narratives that describe, summarize or explain input structured data in a human-like manner at the speed of thousands of pages per second.
Multi-Genre Natural Language Inference (MultiNLI)
Named Entity Recognition (NER)
Heuristic Analysis for NLI Systems (HANS)
Data Related¶
The Stanford Question Answering Dataset (SQuAD)
Situations With Adversarial Generations (SWAG)
Reading Comprehension Dataset (RACE)
The Cross-Lingual NLI Corpus (XNLI)
Model Related¶
Generative Pre-Training (GPT)
Cross-lingual Language Models (XLM)¶
Cross-lingual Natural Language Inference (XNLI)
Causal Language Modeling (CLM)¶
CLM consists of a Transformer to learn text representation by providing a set of previous features. Given the previous hidden state to the current batch, the model predicts the next word.
Masked Language Modeling (MLM)¶
Lample and Connea follow Devlin et al. (2018) approach to pick 15% of subword randomly and replacing it by reserved word ([MASK]) 80% of the time, by a random word 10% of the time and remaining unchanged 10% of the time. The differences between Devlin et al. (2018) are:
Using an arbitrary number of sentences but not pairs of sentences only
Subsample high-frequency subword
Translation Language Modeling (TLM)¶
CLM and MLM are designed for monolingual data while TLM targets on cross-lingual data. BERT use segment embeddings to represent different sentence in a single sequence of input while replace it by language embeddings to represent different language. Subwords are randomly picked in both language data. Both language subword can be leveraged to predict any MASK word.
Out-of-Vocabulary (OOV)¶
Double Dipping in Machine Learning¶
Byte Pair Encoding (BPE) Subword Algorithm¶
Take Away¶
BERT use segment embeddings (represent different sentence) while XLM use language embeddings (represent different language).
CLM does not scale to a cross-lingual scenario.
XLM may not fit for low resource language as if required parallel data (TML) to boost up the performance. Meanwhile, Multilingual Neural Language Models are designed to overcome this limitation.