| | --- |
| | --- |
| | pipeline_tag: text-generation |
| | language: en |
| | library_name: transformers |
| | tags: |
| | - t5 |
| | - grammar-correction |
| | - text-generation |
| | --- |
| | |
| | # T5-REF-CORRUPT-EN: Automatic Error Correction of Academic Referencing According to Institutional Guidelines of the Center for Translation Studies (CTS) of University of Vienna |
| |
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| | **Objective:** This model corrects errors in academic referencing. For example: |
| |
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| | *Input (wrong sentence)*: According to Smith **&** Peterson **2016 56**, the translation reveals patterns that suggest underlying semantic shifts |
| |
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| | *Output (clean sentence)*: According to Smith **and** Peterson **(2016: 56)**, the translation reveals patterns that suggest underlying semantic shifts. |
| |
|
| | **Model Details:** |
| |
|
| | - **Model name:** T5-REF-CORRUPT-EN |
| | - **Base model:** T5-base |
| | - **Language:** English |
| | - **Training data:** Synthetically generated using LLMs and synthetically corrupted real student sentences. |
| |
|
| | **Usage Cases:** Error correction of academic references according to CTS guidelines. |
| |
|
| | ## Example |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
| | |
| | model_id = "elizaveta-dev/T5-REF-CORRUPT-EN" |
| | |
| | tokenizer = AutoTokenizer.from_pretrained(model_id) |
| | model = AutoModelForSeq2SeqLM.from_pretrained(model_id) |
| | |
| | text = "According to Smith & Peterson 2016 56, the translation reveals patterns that suggest underlying semantic shifts." |
| | inputs = tokenizer(text, return_tensors="pt") |
| | |
| | outputs = model.generate(**inputs, max_new_tokens=128) |
| | print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
| | ``` |
| |
|
| | ## Use-Cases |
| |
|
| | The model can perform automatic corrections of various referencing errors, including: |
| |
|
| | ### 1. Incorrect Citation Type (Parenthetical vs. Narrative) |
| |
|
| | *Example of mistake:* (Lopez 2018; Chen 2012) found that cultural context strongly influences translation strategies. |
| |
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| | *Example of correction:* Lopez (2018) and Chen (2012) found that cultural context strongly influences translation strategies. |
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| | *Example of mistake:* This topic has been widely researched Baker (2006). |
| |
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| | *Example of correction:* This topic has been widely researched (Baker 2006). |
| |
|
| | --- |
| |
|
| | ### 2. Incorrect Citation for Two Authors |
| |
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| | *Example of mistake:* The concept of functional equivalence was analyzed by Baker & Green (2007). |
| |
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| | *Example of correction:* The concept of functional equivalence was analyzed by Baker and Green (2007). |
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| | *Example of mistake:* Previous research (Müller, Schmidt 2001) highlights challenges in literary translation. |
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| | *Example of correction:* Previous research (Müller & Schmidt 2001) highlights challenges in literary translation. |
| |
|
| | --- |
| |
|
| | ### 3. Incorrect Placement of Citations |
| |
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| | *Example of mistake:* According to Williams, translation theory continues to evolve (2011: 77). |
| |
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| | *Example of correction:* According to Williams (2011: 77), translation theory continues to evolve. |
| |
|
| | --- |
| |
|
| | ### 4. Redundant Entities |
| |
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| | *Example of mistake:* As Lee (2009) explains, equivalence is central in translation (Lee 2009). |
| |
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| | *Example of correction:* As Lee (2009) explains, equivalence is central in translation. |
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