| Challenge: | Despite the cutting-edge representation learning, most language models focus on specific levels of linguistic units. |
| Approach: | They propose a training objective MiSAD that utilizes meaningful n-grams extracted from large unlabeled corpus by an algorithm for pre-trained language models. |
| Outcome: | The proposed model achieves highest accuracy on analogy tasks in different language levels and significantly improves performance on downstream tasks. |
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Advances in Pre-Training Distributed Word Representations (L18-1)
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| Challenge: | Pre-trained word representations are a building block of many Natural Language Processing and Machine Learning applications. |
| Approach: | They propose to combine known tricks and a set of publicly available pre-trained word vector representations to train high-quality representations. |
| Outcome: | The proposed models outperform the current state of the art on a number of tasks while maintaining a high training speed to scale to massive amount of data. |
Pre-trained language model representations for language generation (N19-1)
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| Challenge: | Pre-trained language model representations have been successful in a wide range of language understanding tasks. |
| Approach: | They propose to use pre-trained language model representations to integrate them into sequence to sequence models and apply it to machine translation and abstractive summarization. |
| Outcome: | The proposed model is able to perform 5.3 BLEU in machine translation and 5.3 on the full text version of CNN/DailyMail. |
General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference (2020.findings-emnlp)
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| Challenge: | Large pre-trained language models are currently used for many NLP tasks . however, inference for these models requires significant computational resources . |
| Approach: | They propose to use a shared text encoder to amortize the computational cost of inference over multiple tasks. |
| Outcome: | The proposed method reduces the size of the extracted representations by a factor of 16 to store them for later use. |
Infusing Finetuning with Semantic Dependencies (2021.tacl-1)
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| Challenge: | Several diagnostics help to localize the benefits of our approach. |
| Approach: | They apply convolutional graph encoders to integrate semantic parses into task-specific finetuning. |
| Outcome: | The proposed approach yields benefits to natural language understanding (NLU) tasks in the GLUE benchmark. |
Discovering Representation Sprachbund For Multilingual Pre-Training (2021.findings-emnlp)
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| Challenge: | Existing models perform poorly on many languages and cross-lingual tasks due to typological differences and contradictions between some languages. |
| Approach: | They propose to pre-train multilingual pre-trained models to handle cross-lingual tasks in one model. |
| Outcome: | The proposed model improves performance on cross-lingual tasks compared to baselines on multiple languages . |
Segmentation-free compositional n-gram embedding (N19-1)
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| Challenge: | Existing word embedding models depend on word segmentation, but this method is difficult when corpora written in noisy or unsegmented languages. |
| Approach: | They propose a new method that models words, phrases and sentences seamlessly without word segmentation. |
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Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models (2020.findings-emnlp)
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| Challenge: | Existing knowledge graph embedding models suffer from limited knowledge representation due to sparse and noisy dataset annotations. |
| Approach: | They propose to use pretrained language models to enhance knowledge representation by leveraging world knowledge from pretrained models. |
| Outcome: | Extensive experiments show that the proposed framework can improve results over existing models. |
Allocating Large Vocabulary Capacity for Cross-Lingual Language Model Pre-Training (2021.emnlp-main)
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| Challenge: | Existing models require a more expressive vocabulary to represent all languages . however, increasing the vocabulary size significantly slows down the pre-training speed . |
| Approach: | They propose an algorithm VoCap to determine the desired vocabulary capacity of each language. |
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Give your Text Representation Models some Love: the Case for Basque (2020.lrec-1)
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Rodrigo Agerri, Iñaki San Vicente, Jon Ander Campos, Ander Barrena, Xabier Saralegi, Aitor Soroa, Eneko Agirre
| Challenge: | Word embeddings and pre-trained language models are expensive to train and are often used by small companies and research groups to build their own. |
| Approach: | They propose to use word embeddings and pre-trained language models to build rich representations of text and improve NLP tasks. |
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Improving the Lexical Ability of Pretrained Language Models for Unsupervised Neural Machine Translation (2021.naacl-main)
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| Challenge: | Existing methods for unsupervised neural machine translation (UNMT) use cross-lingual pretraining to align the lexical- and high-level representations of two languages. |
| Approach: | They propose to use type-level cross-lingual subword embeddings to enhance the bilingual masked language model pretraining with lexical-level information to align the two languages. |
| Outcome: | Empirical results show that the method improves on UNMT (up to 4.5 BLEU) and bilingual lexicon induction compared to baseline models. |