We lifted 6 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| pkunlp-icler/cofftea | canonical | 3 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| convert_examples_to_features_lexical_filter | Ran | pkunlp-icler/cofftea/code/utils.py code served (permissive licence) · get_code("8e4154f686a8e164") |
| convert_examples_to_features_wo_lexical_filter | Ran | pkunlp-icler/cofftea/code/utils.py code served (permissive licence) · get_code("f508b8a818727772") |
| convert_frame_examples_to_features | Ran | pkunlp-icler/cofftea/code/utils.py code served (permissive licence) · get_code("545dc475ede917ea") |
| load_and_cache_examples | Not yet run | pkunlp-icler/cofftea/code/dataset.py code served (permissive licence) · get_code("3de6c82bab9412ea") |
| load_feature_lexical_filter | Not yet run | pkunlp-icler/cofftea/code/dataset.py code served (permissive licence) · get_code("01e4ca023ed397b7") |
| select_field | Not yet run | pkunlp-icler/cofftea/code/dataset.py code served (permissive licence) · get_code("0a546b305d274996") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Frame identification aims to find semantic frames associated with target words in a sentence. Recent researches measure the similarity or matching score between targets and candidate frames by modeling frame definitions. However, they either lack sufficient representation learning of the definitions or face challenges in efficiently selecting the most suitable frame from over 1000 candidate frames. Moreover, commonly used lexicon filtering ($lf$) to obtain candidate frames for the target may ignore out-of-vocabulary targets and cause inadequate frame modeling. In this paper, we propose CoFFTEA, a $\underline{Co}$arse-to-$\underline{F}$ine $\underline{F}$rame and $\underline{T}$arget $\underline{E}$ncoders $\underline{A}$rchitecture. With contrastive learning and dual encoders, CoFFTEA efficiently and effectively models the alignment between frames and targets. By employing a coarse-to-fine curriculum learning procedure, CoFFTEA gradually learns to differentiate frames with varying degrees of similarity. Experimental results demonstrate that CoFFTEA outperforms previous models by 0.93 overall scores and 1.53 R@1 without $lf$. Further analysis suggests that CoFFTEA can better model the relationships between frame and frame, as well as target and target. The code for our approach is available at https://github.com/pkunlp-icler/COFFTEA.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2310.13316")
get_code_for_paper("2310.13316")
have("2310.13316")
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