instruction:
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.
question:
We evaluate our newly proposed models and related baselines in several low-resource settings across different languages with real, distantly supervised data with non-synthetic noise. The advanced modeling of the noisy labels substantially improves the performance up to 36% over methods without noise-handling and up to 9% over all other noise-handling baselines.
Question: Did they evaluate against baseline?
answer:
Yes

question:
To test the robustness of the taggers against the OOV problem, we also conduct experiments using artificially constructed unnormalized text by corrupting words in the normal dev set. Again, the CNN tagger outperforms the two baselines by a very large margin.
Question: How do they confirm their model working well on out-of-vocabulary problems?
answer:
conduct experiments using artificially constructed unnormalized text by corrupting words in the normal dev set

question:
More specifically, we observe the impact of: (i) pre-trained word embeddings BIBREF11, BIBREF12, recurrent BIBREF13 and transformer-based sentence encoders BIBREF14 as question representation strategies; (ii) distinct convolutional neural networks used for visual feature extraction BIBREF15, BIBREF16, BIBREF17; and (iii) standard fusion strategies, as well as the importance of two main attention mechanisms BIBREF18, BIBREF19.
Question: What type of experiments are performed?
answer:
pre-trained word embeddings BIBREF11, BIBREF12 recurrent BIBREF13 transformer-based sentence encoders BIBREF14 distinct convolutional neural networks standard fusion strategies two main attention mechanisms BIBREF18, BIBREF19