On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages

• URL: http://arxiv.org/abs/2011.03965v1
• Date: Sun, 8 Nov 2020 12:15:31 GMT
• Title: On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages
• Authors: Satwik Bhattamishra, Kabir Ahuja, Navin Goyal
• Abstract summary: We study the performance of recurrent models on Dyck-n languages. We find that while recurrent models generalize nearly perfectly if the lengths of the training and test strings are from the same range, they perform poorly if the test strings are longer.
• Score: 9.12267978757844
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While recurrent models have been effective in NLP tasks, their performance on context-free languages (CFLs) has been found to be quite weak. Given that CFLs are believed to capture important phenomena such as hierarchical structure in natural languages, this discrepancy in performance calls for an explanation. We study the performance of recurrent models on Dyck-n languages, a particularly important and well-studied class of CFLs. We find that while recurrent models generalize nearly perfectly if the lengths of the training and test strings are from the same range, they perform poorly if the test strings are longer. At the same time, we observe that recurrent models are expressive enough to recognize Dyck words of arbitrary lengths in finite precision if their depths are bounded. Hence, we evaluate our models on samples generated from Dyck languages with bounded depth and find that they are indeed able to generalize to much higher lengths. Since natural language datasets have nested dependencies of bounded depth, this may help explain why they perform well in modeling hierarchical dependencies in natural language data despite prior works indicating poor generalization performance on Dyck languages. We perform probing studies to support our results and provide comparisons with Transformers.

Related papers

• Investigating Language-Specific Calibration For Pruning Multilingual Large Language Models [11.421452042888523]
  We compare different calibration languages for pruning multilingual models across diverse languages, tasks, models, and SotA pruning techniques. Our results offer practical suggestions, for example, calibrating in the target language can efficiently retain the language modeling capability but does not necessarily benefit downstream tasks.
  arXiv  Detail & Related papers  (2024-08-26T16:29:13Z)
• Towards a theory of how the structure of language is acquired by deep neural networks [6.363756171493383]
  We use a tree-like generative model that captures many of the hierarchical structures found in natural languages. We show that token-token correlations can be used to build a representation of the grammar's hidden variables. We conjecture that the relationship between training set size and effective range of correlations holds beyond our synthetic datasets.
  arXiv  Detail & Related papers  (2024-05-28T17:01:22Z)
• Can Perplexity Predict Fine-Tuning Performance? An Investigation of Tokenization Effects on Sequential Language Models for Nepali [0.0]
  The study of how subwording affects the understanding capacity of language models has been very few and only limited to a handful of languages. We used 6 different tokenization schemes to pretrain relatively small language models in Nepali and used the representations learned to finetune on several downstream tasks.
  arXiv  Detail & Related papers  (2024-04-28T05:26:12Z)
• Language Models for Text Classification: Is In-Context Learning Enough? [54.869097980761595]
  Recent foundational language models have shown state-of-the-art performance in many NLP tasks in zero- and few-shot settings. An advantage of these models over more standard approaches is the ability to understand instructions written in natural language (prompts) This makes them suitable for addressing text classification problems for domains with limited amounts of annotated instances.
  arXiv  Detail & Related papers  (2024-03-26T12:47:39Z)
• Making Retrieval-Augmented Language Models Robust to Irrelevant Context [55.564789967211844]
  An important desideratum of RALMs, is that retrieved information helps model performance when it is relevant. Recent work has shown that retrieval augmentation can sometimes have a negative effect on performance.
  arXiv  Detail & Related papers  (2023-10-02T18:52:35Z)
• Are Large Language Models Robust Coreference Resolvers? [17.60248310475889]
  We show that prompting for coreference can outperform current unsupervised coreference systems. Further investigations reveal that instruction-tuned LMs generalize surprisingly well across domains, languages, and time periods.
  arXiv  Detail & Related papers  (2023-05-23T19:38:28Z)
• Transparency Helps Reveal When Language Models Learn Meaning [71.96920839263457]
  Our systematic experiments with synthetic data reveal that, with languages where all expressions have context-independent denotations, both autoregressive and masked language models learn to emulate semantic relations between expressions. Turning to natural language, our experiments with a specific phenomenon -- referential opacity -- add to the growing body of evidence that current language models do not well-represent natural language semantics.
  arXiv  Detail & Related papers  (2022-10-14T02:35:19Z)
• Language Models are Few-shot Multilingual Learners [66.11011385895195]
  We evaluate the multilingual skills of the GPT and T5 models in conducting multi-class classification on non-English languages. We show that, given a few English examples as context, pre-trained language models can predict not only English test samples but also non-English ones.
  arXiv  Detail & Related papers  (2021-09-16T03:08:22Z)
• Multi-timescale Representation Learning in LSTM Language Models [69.98840820213937]
  Language models must capture statistical dependencies between words at timescales ranging from very short to very long. We derived a theory for how the memory gating mechanism in long short-term memory language models can capture power law decay. Experiments showed that LSTM language models trained on natural English text learn to approximate this theoretical distribution.
  arXiv  Detail & Related papers  (2020-09-27T02:13:38Z)
• Recognizing Long Grammatical Sequences Using Recurrent Networks Augmented With An External Differentiable Stack [73.48927855855219]
  Recurrent neural networks (RNNs) are a widely used deep architecture for sequence modeling, generation, and prediction. RNNs generalize poorly over very long sequences, which limits their applicability to many important temporal processing and time series forecasting problems. One way to address these shortcomings is to couple an RNN with an external, differentiable memory structure, such as a stack. In this paper, we improve the memory-augmented RNN with important architectural and state updating mechanisms.
  arXiv  Detail & Related papers  (2020-04-04T14:19:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.