# Introducing "Forecast Utterance" for Conversational Data Science

arXiv: https://arxiv.org/abs/2309.03877 · HTML: https://mlsanity.com/paper/2309.03877/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 78 references that are in the map.
- First posted on arXiv 7 Sep 2023.

## Builds on (most cited first, 30 of 41)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style](https://mlsanity.com/paper/2106.04619/) (2021)
- [A Review of Generalized Zero-Shot Learning Methods](https://mlsanity.com/paper/2011.08641/) (2020)
- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1609.01454/) (2016)
- [SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness](https://mlsanity.com/paper/2009.10195/) (2020)
- [Joint Slot Filling and Intent Detection via Capsule Neural Networks](https://mlsanity.com/paper/1812.09471/) (2018)
- [DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding](https://mlsanity.com/paper/1709.04696/) (2017)
- [Meta-learning framework with applications to zero-shot time-series forecasting](https://mlsanity.com/paper/2002.02887/) (2020)
- [Domain Adaptation for Time Series Forecasting via Attention Sharing](https://mlsanity.com/paper/2102.06828/) (2021)
- [Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2005.00891/) (2020)
- [AutoML to Date and Beyond: Challenges and Opportunities](https://mlsanity.com/paper/2010.10777/) (2020)
- [GODEL: Large-Scale Pre-Training for Goal-Directed Dialog](https://mlsanity.com/paper/2206.11309/) (2022)
- [Learning to Classify Intents and Slot Labels Given a Handful of Examples](https://mlsanity.com/paper/2004.10793/) (2020)
- [A Unified approach for Conventional Zero-shot, Generalized Zero-shot and Few-shot Learning](https://mlsanity.com/paper/1706.08653/) (2017)
- [Fusing task-oriented and open-domain dialogues in conversational agents](https://mlsanity.com/paper/2109.04137/) (2021)
- [Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2004.03386/) (2020)
- [Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling](https://mlsanity.com/paper/1601.01530/) (2016)
- [On Application of Learning to Rank for E-Commerce Search](https://mlsanity.com/paper/1903.04263/) (2019)
- [Deep Semantic Role Labeling with Self-Attention](https://mlsanity.com/paper/1712.01586/) (2017)
- [ChatGPT as your Personal Data Scientist](https://mlsanity.com/paper/2305.13657/) (2023)
- [Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks](https://mlsanity.com/paper/1707.06799/) (2017)
- [A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog](https://mlsanity.com/paper/2207.13211/) (2022)

## Built on by (most cited first, 1 of 1)

- [Towards Online Multi-Modal Social Interaction Understanding](https://mlsanity.com/paper/2503.19851/) (2025)

## Abstract (arXiv)

Envision an intelligent agent capable of assisting users in conducting forecasting tasks through intuitive, natural conversations, without requiring in-depth knowledge of the underlying machine learning (ML) processes. A significant challenge for the agent in this endeavor is to accurately comprehend the user's prediction goals and, consequently, formulate precise ML tasks. In this paper, we take a pioneering step towards this ambitious goal by introducing a new concept called Forecast Utterance and then focus on the automatic and accurate interpretation of users' prediction goals from these utterances. Specifically, we frame the task as a slot-filling problem, where each slot corresponds to a specific aspect of the goal prediction task. We then employ two zero-shot methods for solving the slot-filling task, namely: 1) Entity Extraction (EE), and 2) Question-Answering (QA) techniques. Our experiments, conducted with three meticulously crafted data sets, validate the viability of our ambitious goal and demonstrate the effectiveness of both EE and QA techniques in interpreting Forecast Utterances.