Building on research arguing for the possibility of conceptual and categorical knowledge acquisition through statistics contained in language, we evaluate predictive language models (LMs) -- informed solely by textual input -- on a prevalent phenomenon in cognitive science: typicality. Inspired by experiments that involve language processing and show robust typicality effects in humans, we propose two tests for LMs. Our first test targets whether typicality modulates LM probabilities in assigning taxonomic category memberships to items. The second test investigates sensitivities to typicality in LMs' probabilities when extending new information about items to their categories. Both tests show modest -- but not completely absent -- correspondence between LMs and humans, suggesting that text-based exposure alone is insufficient to acquire typicality knowledge.
Numbers are essential components of text, like any other word tokens, from which natural language processing (NLP) models are built and deployed. Though numbers are typically not accounted for distinctly in most NLP tasks, there is still an underlying amount of numeracy already exhibited by NLP models. In this work, we attempt to tap this potential of state-of-the-art NLP models and transfer their ability to boost performance in related tasks. Our proposed classification of numbers into entities helps NLP models perform well on several tasks, including a handcrafted Fill-In-The-Blank (FITB) task and on question answering using joint embeddings, outperforming the BERT and RoBERTa baseline classification.
Meta-learning hyperparameter optimization (HPO) algorithms from prior experiments is a promising approach to improve optimization efficiency over objective functions from a similar distribution. However, existing methods are restricted to learning from experiments sharing the same set of hyperparameters. In this paper, we introduce the OptFormer, the first text-based Transformer HPO framework that provides a universal end-to-end interface for jointly learning policy and function prediction when trained on vast tuning data from the wild. Our extensive experiments demonstrate that the OptFormer can imitate at least 7 different HPO algorithms, which can be further improved via its function uncertainty estimates. Compared to a Gaussian Process, the OptFormer also learns a robust prior distribution for hyperparameter response functions, and can thereby provide more accurate and better calibrated predictions. This work paves the path to future extensions for training a Transformer-based model as a general HPO optimizer.
Initial fault detection and diagnostics are imperative measures to improve the efficiency, safety, and stability of vehicle operation. In recent years, numerous studies have investigated data-driven approaches to improve the vehicle diagnostics process using available vehicle data. Moreover, data-driven methods are employed to enhance customer-service agent interactions. In this study, we demonstrate a machine learning pipeline to improve automated vehicle diagnostics. First, Natural Language Processing (NLP) is used to automate the extraction of crucial information from free-text failure reports (generated during customers' calls to the service department). Then, deep learning algorithms are employed to validate service requests and filter vague or misleading claims. Ultimately, different classification algorithms are implemented to classify service requests so that valid service requests can be directed to the relevant service department. The proposed model- Bidirectional Long Short Term Memory (BiLSTM) along with Convolution Neural Network (CNN)- shows more than 18\% accuracy improvement in validating service requests compared to technicians' capabilities. In addition, using domain-based NLP techniques at preprocessing and feature extraction stages along with CNN-BiLSTM based request validation enhanced the accuracy ($>25\%$), sensitivity ($>39\%$), specificity ($>11\%$), and precision ($>11\%$) of Gradient Tree Boosting (GTB) service classification model. The Receiver Operating Characteristic Area Under the Curve (ROC-AUC) reached 0.82.
Transformer-based architectures in natural language processing force input size limits that can be problematic when long documents need to be processed. This paper overcomes this issue for keyphrase extraction by chunking the long documents while keeping a global context as a query defining the topic for which relevant keyphrases should be extracted. The developed system employs a pre-trained BERT model and adapts it to estimate the probability that a given text span forms a keyphrase. We experimented using various context sizes on two popular datasets, Inspec and SemEval, and a large novel dataset. The presented results show that a shorter context with a query overcomes a longer one without the query on long documents.
Large-scale language models pretrained on massive corpora of text, such as GPT-2, are powerful open-domain text generators. However, as our systematic examination reveals, it is still challenging for such models to generate coherent long passages of text ($>$1000 tokens), especially when the models are fine-tuned to the target domain on a small corpus. To overcome the limitation, we propose a simple but effective method of generating text in a progressive manner, inspired by generating images from low to high resolution. Our method first produces domain-specific content keywords and then progressively refines them into complete passages in multiple stages. The simple design allows our approach to take advantage of pretrained language models at each stage and effectively adapt to any target domain given only a small set of examples. We conduct a comprehensive empirical study with a broad set of evaluation metrics, and show that our approach significantly improves upon the fine-tuned GPT-2 in terms of domain-specific quality and sample efficiency. The coarse-to-fine nature of progressive generation also allows for a higher degree of control over the generated content.
Facial micro-expressions recognition has attracted much attention recently. Micro-expressions have the characteristics of short duration and low intensity, and it is difficult to train a high-performance classifier with the limited number of existing micro-expressions. Therefore, recognizing micro-expressions is a challenge task. In this paper, we propose a micro-expression recognition method based on attribute information embedding and cross-modal contrastive learning. We use 3D CNN to extract RGB features and FLOW features of micro-expression sequences and fuse them, and use BERT network to extract text information in Facial Action Coding System. Through cross-modal contrastive loss, we embed attribute information in the visual network, thereby improving the representation ability of micro-expression recognition in the case of limited samples. We conduct extensive experiments in CASME II and MMEW databases, and the accuracy is 77.82% and 71.04%, respectively. The comparative experiments show that this method has better recognition effect than other methods for micro-expression recognition.
An ongoing trend in generative modelling research has been to push sample resolutions higher whilst simultaneously reducing computational requirements for training and sampling. We aim to push this trend further via the combination of techniques - each component representing the current pinnacle of efficiency in their respective areas. These include vector-quantized GAN (VQ-GAN), a vector-quantization (VQ) model capable of high levels of lossy - but perceptually insignificant - compression; hourglass transformers, a highly scaleable self-attention model; and step-unrolled denoising autoencoders (SUNDAE), a non-autoregressive (NAR) text generative model. Unexpectedly, our method highlights weaknesses in the original formulation of hourglass transformers when applied to multidimensional data. In light of this, we propose modifications to the resampling mechanism, applicable in any task applying hierarchical transformers to multidimensional data. Additionally, we demonstrate the scalability of SUNDAE to long sequence lengths - four times longer than prior work. Our proposed framework scales to high-resolutions ($1024 \times 1024$) and trains quickly (2-4 days). Crucially, the trained model produces diverse and realistic megapixel samples in approximately 2 seconds on a consumer-grade GPU (GTX 1080Ti). In general, the framework is flexible: supporting an arbitrary number of sampling steps, sample-wise self-stopping, self-correction capabilities, conditional generation, and a NAR formulation that allows for arbitrary inpainting masks. We obtain FID scores of 10.56 on FFHQ256 - close to the original VQ-GAN in less than half the sampling steps - and 21.85 on FFHQ1024 in only 100 sampling steps.
More and more investors and machine learning models rely on social media (e.g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements. Although text-based models are known to be vulnerable to adversarial attacks, whether stock prediction models have similar vulnerability is underexplored. In this paper, we experiment with a variety of adversarial attack configurations to fool three stock prediction victim models. We address the task of adversarial generation by solving combinatorial optimization problems with semantics and budget constraints. Our results show that the proposed attack method can achieve consistent success rates and cause significant monetary loss in trading simulation by simply concatenating a perturbed but semantically similar tweet.
We propose an effective approach to utilize pretrained speech and text models to perform speech-to-text translation (ST). Our recipe to achieve cross-modal and cross-lingual transfer learning (XMTL) is simple and generalizable: using an adaptor module to bridge the modules pretrained in different modalities, and an efficient finetuning step which leverages the knowledge from pretrained modules yet making it work on a drastically different downstream task. With this approach, we built a multilingual speech-to-text translation model with pretrained audio encoder (wav2vec) and multilingual text decoder (mBART), which achieves new state-of-the-art on CoVoST 2 ST benchmark [1] for English into 15 languages as well as 6 Romance languages into English with on average +2.8 BLEU and +3.9 BLEU, respectively. On low-resource languages (with less than 10 hours training data), our approach significantly improves the quality of speech-to-text translation with +9.0 BLEU on Portuguese-English and +5.2 BLEU on Dutch-English.