
# Paper
This is an mBART-based model that can be used for both multilingual DRS parsing and DRS-to-text generation, covering four languages (English:EN, German:DE,
Italian:IT, Dutch:NL). It is introduced in the paper [Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation](https://arxiv.org/abs/2306.00124).


# Abstract
Pre-trained language models (PLMs) have achieved great success in NLP and have recently been used for tasks in computational semantics. However, these tasks do not fully benefit from PLMs since meaning representations are not explicitly included in the pre-training stage. We introduce multilingual pre-trained language-meaning models based on Discourse Representation Structures (DRSs), including meaning representations besides natural language texts in the same model, and design a new strategy to reduce the gap between the pre-training and fine-tuning objectives. Since DRSs are language neutral, cross-lingual transfer learning is adopted to further improve the performance of non-English tasks. Automatic evaluation results show that our approach achieves the best performance on both the multilingual DRS parsing and DRS-to-text generation tasks. Correlation analysis between automatic metrics and human judgements on the generation task further validates the effectiveness of our model. Human inspection reveals that out-of-vocabulary tokens are the main cause of erroneous results.

# How to use
```bash
git clone https://github.com/wangchunliu/DRS-pretrained-LMM.git
cd DRS-pretrained-LMM
```

```python
# a case of drs-text generation
from tokenization_mlm import MLMTokenizer
from transformers import MBartForConditionalGeneration

# For DRS parsing, src_lang should be set to en_XX, de_DE, it_IT, or nl_XX
tokenizer = MLMTokenizer.from_pretrained('laihuiyuan/DRS-LMM', src_lang='<drs>')
model = MBartForConditionalGeneration.from_pretrained('laihuiyuan/DRS-LMM')

# gold text: The court is adjourned until 3:00 p.m. on March 1st.
inp_ids = tokenizer.encode(
    "court.n.01 time.n.08 EQU now adjourn.v.01 Theme -2 Time -1 Finish +1 time.n.08 ClockTime 15:00 MonthOfYear 3 DayOfMonth 1",
    return_tensors="pt")

# For DRS parsing, the forced bos token here should be <drs> 
foced_ids = tokenizer.encode("en_XX", add_special_tokens=False, return_tensors="pt")
outs = model.generate(input_ids=inp_ids, forced_bos_token_id=foced_ids.item(), num_beams=5, max_length=150)
text = tokenizer.decode(outs[0].tolist(), skip_special_tokens=True, clean_up_tokenization_spaces=False)
```

# Citation Info
```BibTeX
@inproceedings{wang-etal-2023-pre,
    title = "Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation",
    author = "Wang, Chunliu and Lai, Huiyuan and Nissim, Malvina and Bos, Johan",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = July,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
}
```
{"language": ["en"], "license": "apache-2.0"}
transformers pytorch mbart text2text-generation en arxiv:2306.00124 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #mbart #text2text-generation #en #arxiv-2306.00124 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# Paper
This is an mBART-based model that can be used for both multilingual DRS parsing and DRS-to-text generation, covering four languages (English:EN, German:DE,
Italian:IT, Dutch:NL). It is introduced in the paper Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation.


# Abstract
Pre-trained language models (PLMs) have achieved great success in NLP and have recently been used for tasks in computational semantics. However, these tasks do not fully benefit from PLMs since meaning representations are not explicitly included in the pre-training stage. We introduce multilingual pre-trained language-meaning models based on Discourse Representation Structures (DRSs), including meaning representations besides natural language texts in the same model, and design a new strategy to reduce the gap between the pre-training and fine-tuning objectives. Since DRSs are language neutral, cross-lingual transfer learning is adopted to further improve the performance of non-English tasks. Automatic evaluation results show that our approach achieves the best performance on both the multilingual DRS parsing and DRS-to-text generation tasks. Correlation analysis between automatic metrics and human judgements on the generation task further validates the effectiveness of our model. Human inspection reveals that out-of-vocabulary tokens are the main cause of erroneous results.

# How to use




Info

# Paper
This is an mBART-based model that can be used for both multilingual DRS parsing and DRS-to-text generation, covering four languages (English:EN, German:DE,
Italian:IT, Dutch:NL). It is introduced in the paper Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation. # Abstract
Pre-trained language models (PLMs) have achieved great success in NLP and have recently been used for tasks in computational semantics. However, these tasks do not fully benefit from PLMs since meaning representations are not explicitly included in the pre-training stage. We introduce multilingual pre-trained language-meaning models based on Discourse Representation Structures (DRSs), including meaning representations besides natural language texts in the same model, and design a new strategy to reduce the gap between the pre-training and fine-tuning objectives. Since DRSs are language neutral, cross-lingual transfer learning is adopted to further improve the performance of non-English tasks. Automatic evaluation results show that our approach achieves the best performance on both the multilingual DRS parsing and DRS-to-text generation tasks. Correlation analysis between automatic metrics and human judgements on the generation task further validates the effectiveness of our model. Human inspection reveals that out-of-vocabulary tokens are the main cause of erroneous results. # How to use




Info
TAGS
#transformers #pytorch #mbart #text2text-generation #en #arxiv-2306.00124 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # Paper
This is an mBART-based model that can be used for both multilingual DRS parsing and DRS-to-text generation, covering four languages (English:EN, German:DE,
Italian:IT, Dutch:NL). It is introduced in the paper Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation. # Abstract
Pre-trained language models (PLMs) have achieved great success in NLP and have recently been used for tasks in computational semantics. However, these tasks do not fully benefit from PLMs since meaning representations are not explicitly included in the pre-training stage. We introduce multilingual pre-trained language-meaning models based on Discourse Representation Structures (DRSs), including meaning representations besides natural language texts in the same model, and design a new strategy to reduce the gap between the pre-training and fine-tuning objectives. Since DRSs are language neutral, cross-lingual transfer learning is adopted to further improve the performance of non-English tasks. Automatic evaluation results show that our approach achieves the best performance on both the multilingual DRS parsing and DRS-to-text generation tasks. Correlation analysis between automatic metrics and human judgements on the generation task further validates the effectiveness of our model. Human inspection reveals that out-of-vocabulary tokens are the main cause of erroneous results. # How to use




Info
TAGS
#transformers #pytorch #mbart #text2text-generation #en #arxiv-2306.00124 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# Paper
This is an mBART-based model that can be used for both multilingual DRS parsing and DRS-to-text generation, covering four languages (English:EN, German:DE,
Italian:IT, Dutch:NL). It is introduced in the paper Pre-Trained Language-Meaning Models for Multilingual Parsing and Generation.# Abstract
Pre-trained language models (PLMs) have achieved great success in NLP and have recently been used for tasks in computational semantics. However, these tasks do not fully benefit from PLMs since meaning representations are not explicitly included in the pre-training stage. We introduce multilingual pre-trained language-meaning models based on Discourse Representation Structures (DRSs), including meaning representations besides natural language texts in the same model, and design a new strategy to reduce the gap between the pre-training and fine-tuning objectives. Since DRSs are language neutral, cross-lingual transfer learning is adopted to further improve the performance of non-English tasks. Automatic evaluation results show that our approach achieves the best performance on both the multilingual DRS parsing and DRS-to-text generation tasks. Correlation analysis between automatic metrics and human judgements on the generation task further validates the effectiveness of our model. Human inspection reveals that out-of-vocabulary tokens are the main cause of erroneous results.# How to use




Info