Sequence Modeling

Chinese word segmentation is a difficult, im- portant and widely-studied sequence modeling problem. This paper demonstrates the abil- ity of linear-chain conditional random fields (CRFs) to perform robust and accurate …...

Recurrent neural networks, particularly long short-term memory (LSTM), have recently shown to be very effective in a wide range of sequence modeling problems, core to which is effective learning of …...

AbstractAutomatic hashtag annotation plays an important role in content understanding for microblog posts. To date, progress made in this field has been restricted to phrase selection from limited candidates, or …...

AbstractIt is challenging to perform lifelong language learning (LLL) on a stream of different tasks without any performance degradation comparing to the multi-task counterparts. To address this issue, we present …...

AbstractSequence modeling has demonstrated state-of-the-art performance on natural language and document understanding tasks. However, it is challenging to correctly serialize tokens in form-like documents in practice due to their variety …...

AbstractIn sequence modeling, certain tokens are usually less ambiguous than others, and representations of these tokens require fewer refinements for disambiguation. However, given the nature of attention-based models like Transformer …...

AbstractWhen a neural language model (LM) is adapted to perform a new task, what aspects of the task predict the eventual performance of the model? In NLP, systematic features of …...