Arash Einolghozati


El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing
Arash Einolghozati | Abhinav Arora | Lorena Sainz-Maza Lecanda | Anuj Kumar | Sonal Gupta
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Being able to parse code-switched (CS) utterances, such as Spanish+English or Hindi+English, is essential to democratize task-oriented semantic parsing systems for certain locales. In this work, we focus on Spanglish (Spanish+English) and release a dataset, CSTOP, containing 5800 CS utterances alongside their semantic parses. We examine the CS generalizability of various Cross-lingual (XL) models and exhibit the advantage of pre-trained XL language models when data for only one language is present. As such, we focus on improving the pre-trained models for the case when only English corpus alongside either zero or a few CS training instances are available. We propose two data augmentation methods for the zero-shot and the few-shot settings: fine-tune using translate-and-align and augment using a generation model followed by match-and-filter. Combining the few-shot setting with the above improvements decreases the initial 30-point accuracy gap between the zero-shot and the full-data settings by two thirds.

EASE: Extractive-Abstractive Summarization End-to-End using the Information Bottleneck Principle
Haoran Li | Arash Einolghozati | Srinivasan Iyer | Bhargavi Paranjape | Yashar Mehdad | Sonal Gupta | Marjan Ghazvininejad
Proceedings of the Third Workshop on New Frontiers in Summarization

Current abstractive summarization systems outperform their extractive counterparts, but their widespread adoption is inhibited by the inherent lack of interpretability. Extractive summarization systems, though interpretable, suffer from redundancy and possible lack of coherence. To achieve the best of both worlds, we propose EASE, an extractive-abstractive framework that generates concise abstractive summaries that can be traced back to an extractive summary. Our framework can be applied to any evidence-based text generation problem and can accommodate various pretrained models in its simple architecture. We use the Information Bottleneck principle to jointly train the extraction and abstraction in an end-to-end fashion. Inspired by previous research that humans use a two-stage framework to summarize long documents (Jing and McKeown, 2000), our framework first extracts a pre-defined amount of evidence spans and then generates a summary using only the evidence. Using automatic and human evaluations, we show that the generated summaries are better than strong extractive and extractive-abstractive baselines.

Getting to Production with Few-shot Natural Language Generation Models
Peyman Heidari | Arash Einolghozati | Shashank Jain | Soumya Batra | Lee Callender | Ankit Arun | Shawn Mei | Sonal Gupta | Pinar Donmez | Vikas Bhardwaj | Anuj Kumar | Michael White
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

In this paper, we study the utilization of pre-trained language models to enable few-shotNatural Language Generation (NLG) in task-oriented dialog systems. We introduce a system consisting of iterative self-training and an extensible mini-template framework that textualizes the structured input data into semi-natural text to fully take advantage of pre-trained language models. We compare var-ious representations of NLG models’ input and output and show that transforming the input and output to be similar to what the language model has seen before during pre-training improves the model’s few-shot performance substantially. We show that neural mod-els can be trained with as few as 300 annotated examples while providing high fidelity, considerably lowering the resource requirements for standing up a new domain or language.This level of data efficiency removes the need for crowd-sourced data collection resulting in higher quality data annotated by expert linguists. In addition, model maintenance and debugging processes will improve in this few-shot setting. Finally, we explore distillation and using a caching system to satisfy latency requirements of real-world systems.


Sound Natural: Content Rephrasing in Dialog Systems
Arash Einolghozati | Anchit Gupta | Keith Diedrick | Sonal Gupta
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We introduce a new task of rephrasing for a more natural virtual assistant. Currently, virtual assistants work in the paradigm of intent-slot tagging and the slot values are directly passed as-is to the execution engine. However, this setup fails in some scenarios such as messaging when the query given by the user needs to be changed before repeating it or sending it to another user. For example, for queries like ‘ask my wife if she can pick up the kids’ or ‘remind me to take my pills’, we need to rephrase the content to ‘can you pick up the kids’ and ‘take your pills’. In this paper, we study the problem of rephrasing with messaging as a use case and release a dataset of 3000 pairs of original query and rephrased query. We show that BART, a pre-trained transformers-based masked language model, is a strong baseline for the task, and show improvements by adding a copy-pointer and copy loss to it. We analyze different trade-offs of BART-based and LSTM-based seq2seq models, and propose a distilled LSTM-based seq2seq as the best practical model

Improving Text-to-Text Pre-trained Models for the Graph-to-Text Task
Zixiaofan Yang | Arash Einolghozati | Hakan Inan | Keith Diedrick | Angela Fan | Pinar Donmez | Sonal Gupta
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)

Converting a knowledge graph or sub-graph to natural text is useful when answering questions based on a knowledge base. High-capacity language models pre-trained on large-scale text corpora have recently been shown to be powerful when fine-tuned for the knowledge-graph-to-text (KG-to-text) task. In this paper, we propose two classes of methods to improve such pre-trained models for this task. First, we improve the structure awareness of the model by organizing the input as well as learning optimal ordering via multitask learning. Second, we bridge the domain gap between text-to-text and KG-to-text tasks via a second-phase KG-to-text pre-training on similar datasets and extra lexicalization supervision to make the input more similar to natural text. We demonstrate the efficacy of our methods on the popular WebNLG dataset. Our best model achieves an almost 3 point BLEU improvement on a strong baseline while lowering the relative slot-error-rate by around 35%. We also validate our results via human evaluation.