Design Challenges and Misconceptions in Neural Sequence Labeling
We investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct a systematic model comparison on three benchmarks (i.e. NER, Chunking, and POS tagging). Misconceptions and inconsistent conclusions in existing literature are examined and clarified under statistical experiments. In the comparison and analysis process, we reach several practical conclusions which can be useful to practitioners.
Code (2)
Tasks
ChunkingMisconceptionsNERPOSPOS TaggingSimilar Papers 제목 키워드 기반
Can Large Language Models Provide Security & Privacy Advice? Measuring the Ability of LLMs to Refute Misconceptions
Users seek security & privacy (S&P) advice from online resources, including trusted websites and content-sharing platforms. These resources help users understand S&P technologies and tools and suggest actionable strategi…
MisconceptionsMultiple-choiceToward Semi-Automatic Misconception Discovery Using Code Embeddings
Understanding students' misconceptions is important for effective teaching and assessment. However, discovering such misconceptions manually can be time-consuming and laborious. Automated misconception discovery can addr…
Code ClassificationMisconceptionsFinnish 5th and 6th graders' misconceptions about Artificial Intelligence
Research on children's initial conceptions of AI is in an emerging state, which, from a constructivist viewpoint, challenges the development of pedagogically sound AI-literacy curricula, methods, and materials. To contri…
MisconceptionsClarifying System 1 & 2 through the Common Model of Cognition
There have been increasing challenges to dual-system descriptions of System-1 and System-2, critiquing them as imprecise and fostering misconceptions. We address these issues here by way of Dennett's appeal to use comput…
MisconceptionsMcMining: Automated Discovery of Misconceptions in Student Code
When learning to code, students often develop misconceptions about various programming language concepts. These can not only lead to bugs or inefficient code, but also slow down the learning of related concepts. In this …