Photo Credit Motivation I’m trying to build a NLP system that can automatically highlight the important part of an article to help people to read long articles. The common practice is to start with a simple baseline model that is useful enough, and then incrementally improves the performance. The TextRank algorithm[1], which I also used as a baseline in a text summarization system, is a natural fit to this task. ...
Photo Credit Overview Part one gave an overview on how OpenNMT-py produces output sequences for a batch of input sequences (Translator._translate_batch method), and how it conducts beam searches (Beam objects): Implementing Beam Search (Part 1) - A Source Code Analysis of OpenNMT-py Now we turn our attention to some of the details we skipped through in part one — the advanced features that influence how the translator produce output candidates/hypotheses. They can be put into two categories: rule-based and number-based. ...
Photo Credit As hinted in the previous post “Building a Summary System in Minutes”, I’ll try do some source code analysis of OpenNMT-py project in this post. I’d like to start with its Beam Search implementation. It is widely used in seq2seq models, but I haven’t yet had a good grasp on its details. The translator/predictor of OpenNMT-py is also one of the most powerful I’ve seen, coming with a wide range of parameters and options. ...
Photo Credit Motivation I was reading this paper titled “Character-Level Language Modeling with Deeper Self-Attention” by Al-Rfou et al., which describes some ways to use Transformer self-attention models to solve the language modeling problem. One big problem of Transformer models in this setting is that they cannot pass information from one batch to the next, so they have to make predictions based on limited contexts. It becomes a problem when we have to compare the results with “traditional” RNN-based models, and what Al-Rfou et al. proposed is to use only the outputs at the last position in the sequence from the Transformers when evaluating. If we ignore the first batch, a sequence of length N will require N batches to predict for Transformers, and only (N / M) batches for RNN models (M being the sequence length of a batch). ...
Photo Credit Joel Grus offered his critique of Jupyter Notebook in a recent talk. I think most of his points are valid and recommend you to read the slides or watch the talk. However, one thing that caught my attention is Mr. Grus’s Python IDE(Visual Studio Code). It looks so good that I decided to give it a try, which led to this blog post. (I Don’t Like Notebooks - Joel Grus - #JupyterCon 2018) ...