Papers by Paul Smolensky
How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVEN (2023.tacl-1)
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| Challenge: | Current language models generate high-quality text, but are they copying it or have they learned generalizable linguistic abstractions? |
| Approach: | They propose a suite of analyses for assessing the novelty of generated text . they focus on sequential structure (n-grams) and syntactic structure (syntactical structure). |
| Outcome: | The proposed model-generated text is as novel as the baseline human-generated model- generated text, but it is copied substantially, the authors show . |
Tensor Product Generation Networks for Deep NLP Modeling (N18-1)
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| Challenge: | Using Tensor Product Representations (TPRs) we propose a new architecture for natural language processing based on the principle that hypothesis space for learning includes network hypotheses that are independently known to be suitable for performing the target task. |
| Approach: | They propose a Tensor Product Generation Network (TPGN) which is capable of carrying out TPR computation but uses unconstrained deep learning to design its internal representations. |
| Outcome: | The proposed architecture outperforms baselines on the COCO dataset and can interpret internal representations and operations. |
Invertible Tree Embeddings using a Cryptographic Role Embedding Scheme (2020.coling-main)
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| Challenge: | Unlike previous attempts, this method does not come at the cost of intractable representation size; it works well when there is sufficient randomness in the representation scheme for simple data and providing an upper bound on its error. |
| Approach: | They propose a method for embedding trees in a vector space based on Tensor-Product Representations (TPRs) that allows for inversion: the retrieval of the original tree structure and nodes from the vectorial embeddment. |
| Outcome: | The proposed method can provide invertibility with error 1% that previous methods would require 8.6 1057 dimensions to represent. |
Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization (2021.naacl-main)
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Yichen Jiang, Asli Celikyilmaz, Paul Smolensky, Paul Soulos, Sudha Rao, Hamid Palangi, Roland Fernandez, Caitlin Smith, Mohit Bansal, Jianfeng Gao
| Challenge: | Abstractive summarization is the task of generating a concise summary of input documents . a middle-aged man and a young girl died after they were unable to avoid the plane . |
| Approach: | They propose a model that enriches the original Transformer with a Tensor Product Representation for abstractive summarization. |
| Outcome: | The proposed model outperforms the Transformer and the original TP-Transformer significantly on several datasets. |
Toward Compositional Behavior in Neural Models: A Survey of Current Views (2024.emnlp-main)
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| Challenge: | Compositionality is a core property of natural language, and it is regarded as a key goal for modern NLP systems. |
| Approach: | They propose a conceptual framework to address compositionality in NLP . they propose to use this framework to survey researchers active in this area . |
| Outcome: | The proposed framework finds consensus on key points and suggests that scale alone is unlikely to achieve the desired behavior. |