Interpreting convolutional networks trained on textual data

• URL: http://arxiv.org/abs/2010.13585v1
• Date: Tue, 20 Oct 2020 20:12:05 GMT
• Title: Interpreting convolutional networks trained on textual data
• Authors: Reza Marzban, Christopher John Crick
• Abstract summary: We train a convolutional model on textual data and analyze the global logic of the model by studying its filter values. We find the most important words in our corpus to our models logic and remove the rest. New models trained on just the 5% most important words can achieve the same performance as the original model while reducing training time by more than half.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There have been many advances in the artificial intelligence field due to the emergence of deep learning. In almost all sub-fields, artificial neural networks have reached or exceeded human-level performance. However, most of the models are not interpretable. As a result, it is hard to trust their decisions, especially in life and death scenarios. In recent years, there has been a movement toward creating explainable artificial intelligence, but most work to date has concentrated on image processing models, as it is easier for humans to perceive visual patterns. There has been little work in other fields like natural language processing. In this paper, we train a convolutional model on textual data and analyze the global logic of the model by studying its filter values. In the end, we find the most important words in our corpus to our models logic and remove the rest (95%). New models trained on just the 5% most important words can achieve the same performance as the original model while reducing training time by more than half. Approaches such as this will help us to understand NLP models, explain their decisions according to their word choices, and improve them by finding blind spots and biases.

Related papers

• Longer Fixations, More Computation: Gaze-Guided Recurrent Neural Networks [12.57650361978445]
  Humans read texts at a varying pace, while machine learning models treat each token in the same way. In this paper, we convert this intuition into a set of novel models with fixation-guided parallel RNNs or layers. We find that, interestingly, the fixation duration predicted by neural networks bears some resemblance to humans' fixation.
  arXiv  Detail & Related papers  (2023-10-31T21:32:11Z)
• Visual Grounding Helps Learn Word Meanings in Low-Data Regimes [47.7950860342515]
  Modern neural language models (LMs) are powerful tools for modeling human sentence production and comprehension. But to achieve these results, LMs must be trained in distinctly un-human-like ways. Do models trained more naturalistically -- with grounded supervision -- exhibit more humanlike language learning? We investigate this question in the context of word learning, a key sub-task in language acquisition.
  arXiv  Detail & Related papers  (2023-10-20T03:33:36Z)
• Meta predictive learning model of languages in neural circuits [2.5690340428649328]
  We propose a mean-field learning model within the predictive coding framework. Our model reveals that most of the connections become deterministic after learning. Our model provides a starting point to investigate the connection among brain computation, next-token prediction and general intelligence.
  arXiv  Detail & Related papers  (2023-09-08T03:58:05Z)
• Why can neural language models solve next-word prediction? A mathematical perspective [53.807657273043446]
  We study a class of formal languages that can be used to model real-world examples of English sentences. Our proof highlights the different roles of the embedding layer and the fully connected component within the neural language model.
  arXiv  Detail & Related papers  (2023-06-20T10:41:23Z)
• Sentence Level Curriculum Learning for Improved Neural Conversational Models [0.0]
  We study how to design machine intelligence to converse with a human user. Our goal is to separate training into segments, with each segment's corpus comprised of longer sentence pairs. This will mimic the desired "buildup" component of human learning.
  arXiv  Detail & Related papers  (2023-05-15T17:28:59Z)
• Effective End-to-End Vision Language Pretraining with Semantic Visual Loss [58.642954383282216]
  Current vision language pretraining models are dominated by methods using region visual features extracted from object detectors. We introduce three types of visual losses that enable much faster convergence and better finetuning accuracy. Compared with region feature models, our end-to-end models could achieve similar or better performance on downstream tasks and run more than 10 times faster during inference.
  arXiv  Detail & Related papers  (2023-01-18T00:22:49Z)
• Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding [82.46024259137823]
  We propose a cross-model comparative loss for a broad range of tasks. We demonstrate the universal effectiveness of comparative loss through extensive experiments on 14 datasets from 3 distinct NLU tasks.
  arXiv  Detail & Related papers  (2023-01-10T03:04:27Z)
• Discovering Latent Knowledge in Language Models Without Supervision [72.95136739040676]
  Existing techniques for training language models can be misaligned with the truth. We propose directly finding latent knowledge inside the internal activations of a language model in a purely unsupervised way. We show that despite using no supervision and no model outputs, our method can recover diverse knowledge represented in large language models.
  arXiv  Detail & Related papers  (2022-12-07T18:17:56Z)
• Visualizing and Explaining Language Models [0.0]
  Natural Language Processing has become, after Computer Vision, the second field of Artificial Intelligence. This paper showcases the techniques used in some of the most popular Deep Learning for NLP visualizations, with a special focus on interpretability and explainability.
  arXiv  Detail & Related papers  (2022-04-30T17:23:33Z)
• Mechanisms for Handling Nested Dependencies in Neural-Network Language Models and Humans [75.15855405318855]
  We studied whether a modern artificial neural network trained with "deep learning" methods mimics a central aspect of human sentence processing. Although the network was solely trained to predict the next word in a large corpus, analysis showed the emergence of specialized units that successfully handled local and long-distance syntactic agreement. We tested the model's predictions in a behavioral experiment where humans detected violations in number agreement in sentences with systematic variations in the singular/plural status of multiple nouns.
  arXiv  Detail & Related papers  (2020-06-19T12:00:05Z)

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.