David Esiobu, Xiaoqing Tan, Saghar Hosseini, Megan Ung, Yuchen Zhang, Jude Fernandes, Jane Dwivedi-Yu, Eleonora Presani, Adina Williams, Eric Smith. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

We study the task of semantic parse correction with natural language feedback.Given a natural language utterance, most semantic parsing systems pose the problem as one-shot translation where the utterance is mapped to a corresponding logical form.In this paper, we investigate a more interactive scenario where humans can further interact with the system by providing free-form natural language feedback to correct the system when it generates an inaccurate interpretation of an initial utterance.We focus on natural language to SQL systems and construct, SPLASH, a dataset of utterances, incorrect SQL interpretations and the corresponding natural language feedback.We compare various reference models for the correction task and show that incorporating such a rich form of feedback can significantly improve the overall semantic parsing accuracy while retaining the flexibility of natural language interaction.While we estimated human correction accuracy is 81.5%, our best model achieves only 25.1%, which leaves a large gap for improvement in future research.SPLASH is publicly available at https:// aka.ms/Splash_dataset. Exact Match Accuracy (%) BaselineCorrection End-to-End Without Feedback ⇒ Seq2Struct N/A 41.30 ⇒ Re-

Multilingual representations embed words from many languages into a single semantic space such that words with similar meanings are close to each other regardless of the language.These embeddings have been widely used in various settings, such as cross-lingual transfer, where a natural language processing (NLP) model trained on one language is deployed to another language.While the crosslingual transfer techniques are powerful, they carry gender bias from the source to target languages.In this paper, we study gender bias in multilingual embeddings and how it affects transfer learning for NLP applications.We create a multilingual dataset for bias analysis and propose several ways for quantifying bias in multilingual representations from both the intrinsic and extrinsic perspectives.Experimental results show that the magnitude of bias in the multilingual representations changes differently when we align the embeddings to different target spaces and that the alignment direction can also have an influence on the bias in transfer learning.We further provide recommendations for using the multilingual word representations for downstream tasks.

Email continues to be one of the most important means of online communication. People spend a significant amount of time sending, reading, searching and responding to email in order to manage tasks, exchange information, etc. In this paper, we study intent identification in workplace email. We use a large scale publicly available email dataset to characterize intents in enterprise email and propose methods for improving intent identification in email conversations. Previous work focused on classifying email messages into broad topical categories or detecting sentences that contain action items or follow certain speech acts. In this work, we focus on sentence-level intent identification and study how incorporating more context (such as the full message body and other metadata) could improve the performance of the intent identification models. We experiment with several models for leveraging context including both classical machine learning and deep learning approaches. We show that modeling the interaction between sentence and context can significantly improve the performance.

Stochastic Dual Coordinate Ascent (SDCA) has recently emerged as a state-of-the-art method for solving large-scale supervised learning problems formulated as minimization of convex loss functions. It performs iterative, random-coordinate updates to maximize the dual objective. Due to the sequential nature of the iterations, it is typically implemented as a single-threaded algorithm limited to in-memory datasets. In this paper, we introduce an asynchronous parallel version of the algorithm, analyze its convergence properties, and propose a solution for primal-dual synchronization required to achieve convergence in practice. In addition, we describe a method for scaling the algorithm to out-of-memory datasets via multi-threaded deserialization of block-compressed data. This approach yields sufficient pseudo-randomness to provide the same convergence rate as random-order in-memory access. Empirical evaluation demonstrates the efficiency of the proposed methods and their ability to fully utilize computational resources and scale to out-of-memory datasets.