In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Based on annotated corpora and token-based features, studies used machine learning approaches to build word segmentation systems with accuracy about 94%-97%. 
 Question: How successful are the approaches used to solve word segmentation in Vietnamese?
Their accuracy in word segmentation is about 94%-97%.

We introduce a surrogate training objective that avoids these problems and as a result is fully continuous.  Specifically, we form a continuous function softLB that seeks to approximate the result of running our decoder on input INLINEFORM0 and then evaluating the result against INLINEFORM1 using INLINEFORM2 . 
 Question: Do they provide a framework for building a sub-differentiable for any final loss metric?
Yes

 Conditional Random Fields
Conditional Random Fields (CRF) BIBREF10 are a standard approach when dealing with sequential data in the context of sequence labeling. BiLSTM-CRF
Prior to the emergence of deep neural language models, BiLSTM-CRF models BIBREF11 had achieved state-of-the-art results for the task of sequence labeling. Multi-Task Learning
Multi-Task Learning (MTL) BIBREF15 has become popular with the progress in deep learning. BioBERT
Deep neural language models have recently evolved to a successful method for representing text. In particular, Bidirectional Encoder Representations from Transformers (BERT) outperformed previous state-of-the-art methods by a large margin on various NLP tasks BIBREF17. 
 Question: What baselines do they introduce?

Conditional Random Fields BiLSTM-CRF Multi-Task Learning BioBERT