We lifted 7 functions out of this paper's own repositories and ran 6 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 |
|---|---|---|
| deepopinion/domain-adapted-atsc | canonical | 0 of 1 |
| ardyh/bert-ada | pwc_unofficial | 6 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| convert_examples_to_features | Ran | ardyh/bert-ada/finetuning_and_classification/utils_glue.py code served (permissive licence) · get_code("ed04fa9adc7b8bad") |
| create_instances_from_document | Ran | ardyh/bert-ada/finetuning_and_classification/pregenerate_training_data.py code served (permissive licence) · get_code("1243fa98fb78271d") |
| create_masked_lm_predictions | Ran | ardyh/bert-ada/finetuning_and_classification/pregenerate_training_data.py code served (permissive licence) · get_code("9f94d1d09b49e09e") |
| generate_qa_sentence_pairs_nosampling | Ran | ardyh/bert-ada/finetuning_and_classification/utils_glue.py code served (permissive licence) · get_code("c987c7b3fcee5ba4") |
| semeval2014term_to_aspectsentiment_hr | Ran | ardyh/bert-ada/finetuning_and_classification/utils_glue.py code served (permissive licence) · get_code("475bf1a7164076b3") |
| semeval2014term_to_aspectsentiment_hr | Ran | ardyh/bert-ada/finetuning_and_classification/utils_glue_ardy.py code served (permissive licence) · get_code("72cb221552ac14db") |
| convert_example_to_features | Not yet run | deepopinion/domain-adapted-atsc/finetuning_and_classification/finetune_on_pregenerated.py code served (permissive licence) · get_code("e442357dd18171c6") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Aspect-Target Sentiment Classification (ATSC) is a subtask of Aspect-Based Sentiment Analysis (ABSA), which has many applications e.g. in e-commerce, where data and insights from reviews can be leveraged to create value for businesses and customers. Recently, deep transfer-learning methods have been applied successfully to a myriad of Natural Language Processing (NLP) tasks, including ATSC. Building on top of the prominent BERT language model, we approach ATSC using a two-step procedure: self-supervised domain-specific BERT language model finetuning, followed by supervised task-specific finetuning. Our findings on how to best exploit domain-specific language model finetuning enable us to produce new state-of-the-art performance on the SemEval 2014 Task 4 restaurants dataset. In addition, to explore the real-world robustness of our models, we perform cross-domain evaluation. We show that a cross-domain adapted BERT language model performs significantly better than strong baseline models like vanilla BERT-base and XLNet-base. Finally, we conduct a case study to interpret model prediction errors.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1908.11860")
get_code_for_paper("1908.11860")
have("1908.11860")
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