We explore the use of large language models (LLMs) for zero-shot semantic parsing.Semantic parsing involves mapping natural language utterances to task-specific meaning representations.LLMs are generally trained on publicly available text and code and cannot be expected to directly generalize to domain-specific parsing tasks in a zero-shot setting.In this work, we propose ZEROTOP, a zero-shot task-oriented parsing method that decomposes semantic parsing problem into a set of abstractive and extractive question-answering (QA) problems.For each utterance, we prompt the LLM with questions corresponding to its top-level intent and a set of slots and use the LLM generations to construct the target meaning representation.We observe that current LLMs fail to detect unanswerable questions; and as a result, cannot handle questions corresponding to missing slots.We address this by fine-tuning a language model on public QA datasets using synthetic negative samples.Experimental results show that our QA-based decomposition paired with the fine-tuned LLM can zero-shot parse ≈ 16% of utterances in the MTOP dataset. * Work done during an internship at Microsoft Semantic Machines.♠ Work done while at Microsoft Semantic Machines.

The ability of generative language models (GLMs) to generate text has improved considerably in the last few years, enabling their use for generative data augmentation.In this work, we propose CONDA, an approach to further improve GLMs' ability to generate synthetic data by reformulating data generation as context generation for a given question-answer (QA) pair and leveraging QA datasets for training context generators.Then, we cast downstream tasks into the same question answering format and adapt the fine-tuned context generators to the target task domain.Finally, we use the fine-tuned GLM to generate relevant contexts, which are in turn used as synthetic training data for their corresponding tasks.We perform extensive experiments on multiple classification datasets and demonstrate substantial improvements in performance for both few-and zeroshot settings.Our analysis reveals that QA datasets that require high-level reasoning abilities (e.g., abstractive and common-sense QA datasets) tend to give the best boost in performance in both few-shot and zero-shot settings.

Existing text classification methods mainly focus on a fixed label set, whereas many realworld applications require extending to new fine-grained classes as the number of samples per label increases.To accommodate such requirements, we introduce a new problem called coarse-to-fine grained classification, which aims to perform fine-grained classification on coarsely annotated data.Instead of asking for new fine-grained human annotations, we opt to leverage label surface names as the only human guidance and weave in rich pretrained generative language models into the iterative weak supervision strategy.Specifically, we first propose a label-conditioned finetuning formulation to attune these generators for our task.Furthermore, we devise a regularization objective based on the coarse-fine label constraints derived from our problem setting, giving us even further improvements over the prior formulation.Our framework uses the fine-tuned generative models to sample pseudo-training data for training the classifier, and bootstraps on real unlabeled data for model refinement.Extensive experiments and case studies on two real-world datasets demonstrate superior performance over SOTA zeroshot classification baselines.

In this paper, we explore text classification with extremely weak supervision, i.e., only relying on the surface text of class names.This is a more challenging setting than the seed-driven weak supervision, which allows a few seed words per class.We opt to attack this problem from a representation learning perspective-ideal document representations should lead to nearly the same results between clustering and the desired classification.In particular, one can classify the same corpus differently (e.g., based on topics and locations), so document representations should be adaptive to the given class names.We propose a novel framework X-Class to realize the adaptive representations.Specifically, we first estimate class representations by incrementally adding the most similar word to each class until inconsistency arises.Following a tailored mixture of class attention mechanisms, we obtain the document representation via a weighted average of contextualized word representations.With the prior of each document assigned to its nearest class, we then cluster and align the documents to classes.Finally, we pick the most confident documents from each cluster to train a text classifier.Extensive experiments demonstrate that X-Class can rival and even outperform seed-driven weakly supervised methods on 7 benchmark datasets.

Weakly supervised text classification based on a few user-provided seed words has recently attracted much attention from researchers.Existing methods mainly generate pseudo-labels in a context-free manner (e.g., string matching), therefore, the ambiguous, context-dependent nature of human language has been long overlooked.In this paper, we propose a novel framework ConWea, providing contextualized weak supervision for text classification.Specifically, we leverage contextualized representations of word occurrences and seed word information to automatically differentiate multiple interpretations of the same word, and thus create a contextualized corpus.This contextualized corpus is further utilized to train the classifier and expand seed words in an iterative manner.This process not only adds new contextualized, highly label-indicative keywords but also disambiguates initial seed words, making our weak supervision fully contextualized.Extensive experiments and case studies on real-world datasets demonstrate the necessity and significant advantages of using contextualized weak supervision, especially when the class labels are fine-grained.

Recent advances in weakly supervised learning enable training high-quality text classifiers by only providing a few user-provided seed words.Existing methods mainly use text data alone to generate pseudo-labels despite the fact that metadata information (e.g., author and timestamp) is widely available across various domains.Strong label indicators exist in the metadata and it has been long overlooked mainly due to the following challenges: (1) metadata is multi-typed, requiring systematic modeling of different types and their combinations, (2) metadata is noisy, some metadata entities (e.g., authors, venues) are more compelling label indicators than others.In this paper, we propose a novel framework, META, which goes beyond the existing paradigm and leverages metadata as an additional source of weak supervision.Specifically, we organize the text data and metadata together into a text-rich network and adopt network motifs to capture appropriate combinations of metadata.Based on seed words, we rank and filter motif instances to distill highly label-indicative ones as "seed motifs", which provide additional weak supervision.Following a bootstrapping manner, we train the classifier and expand the seed words and seed motifs iteratively.Extensive experiments and case studies on real-world datasets demonstrate superior performance and significant advantages of leveraging metadata as weak supervision.

We present a feature vector formation technique for documents -Sparse Composite Document Vector (SCDV)which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation.In SCDV, word embeddings are clustered to capture multiple semantic contexts in which words occur.They are then chained together to form document topic-vectors that can express complex, multi-topic documents.Through extensive experiments on multi-class and multi-label classification tasks, we outperform the previous state-of-the-art method, NTSG (Liu et al., 2015a).We also show that SCDV embeddings perform well on heterogeneous tasks like Topic Coherence, context-sensitive Learning and Information Retrieval.Moreover, we achieve significant reduction in training and prediction times compared to other representation methods.SCDV achieves best of both worlds -better performance with lower time and space complexity.