We present IndoNLI, the first human-elicited NLI dataset for Indonesian.We adapt the data collection protocol for MNLI and collect ∼18K sentence pairs annotated by crowd workers and experts.The expert-annotated data is used exclusively as a test set.It is designed to provide a challenging test-bed for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural changes, idioms, or temporal and spatial reasoning.Experiment results show that XLM-R outperforms other pretrained models in our data.The best performance on the expert-annotated data is still far below human performance (13.4% accuracy gap), suggesting that this test set is especially challenging.Furthermore, our analysis shows that our expert-annotated data is more diverse and contains fewer annotation artifacts than the crowd-annotated data.We hope this dataset can help accelerate progress in Indonesian NLP research.

In recent years, fostered by deep learning technologies and by the high demand for conversational AI, various approaches have been proposed that address the capacity to elicit and understand user's needs in task-oriented dialogue systems.We focus on two core tasks, slot filling (SF) and intent classification (IC), and survey how neural based models have rapidly evolved to address natural language understanding in dialogue systems.We introduce three neural architectures: independent models, which model SF and IC separately, joint models, which exploit the mutual benefit of the two tasks simultaneously, and transfer learning models, that scale the model to new domains.We discuss the current state of the research in SF and IC, and highlight challenges that still require attention.

Slot filling is a core operation for utterance understanding in task-oriented dialogue systems.Slots are typically domain-specific, and adding new domains to a dialogue system involves data and time-intensive processes.A popular technique to address the problem is transfer learning, where it is assumed the availability of a large slot filling dataset for the source domain, to be used to help slot filling on the target domain, with fewer data.In this work, instead, we propose to leverage source tasks based on semantically related non-conversational resources (e.g., semantic sequence tagging datasets), as they are both cheaper to obtain and reusable to several slot filling domains.We show that using auxiliary non-conversational tasks in a multi-task learning setup consistently improves low resource slot filling performance.

For AI systems to reason about real world situations, they need to recognize which processes are at play and which entities play key roles in them.Our goal is to extract this kind of rolebased knowledge about processes, from multiple sentence-level descriptions.This knowledge is hard to acquire; while semantic role labeling (SRL) systems can extract sentence level role information about individual mentions of a process, their results are often noisy and they do not attempt create a globally consistent characterization of a process.To overcome this, we extend standard within sentence joint inference to inference across multiple sentences.This cross sentence inference promotes role assignments that are compatible across different descriptions of the same process.When formulated as an Integer Linear Program, this leads to improvements over within-sentence inference by nearly 3% in F1.The resulting role-based knowledge is of high quality (with a F1 of nearly 82).