Indirect User Requests (IURs), such as “It’s cold in here” instead of “Could you please increase the temperature?” are common in human-human task-oriented dialogue and require world knowledge and pragmatic reasoning from the listener. While large language models (LLMs) can handle these requests effectively, smaller models deployed on virtual assistants often struggle due to resource constraints. Moreover, existing task-oriented dialogue benchmarks lack sufficient examples of complex discourse phenomena such as indirectness. To address this, we propose a set of linguistic criteria along with an LLM-based pipeline for generating realistic IURs to test natural language understanding (NLU) and dialogue state tracking (DST) models before deployment in a new domain. We also release IndirectRequests, a dataset of IURs based on the Schema-Guided Dialogue (SGD) corpus, as a comparative testbed for evaluating the performance of smaller models in handling indirect requests.

Referring image segmentation aims to localize the object in an image referred by a natural language expression. Most previous studies learn referring image segmentation with a large-scale dataset containing segmentation labels, but they are costly. We present a weakly supervised learning method for referring image segmentation that only uses readily available image-text pairs. We first train a visual-linguistic model for image-text matching and extract a visual saliency map through Grad-CAM to identify the image regions corresponding to each word. However, we found two major problems with Grad-CAM. First, it lacks consideration of critical semantic relationships between words. We tackle this problem by modeling the relationship between words through intra-chunk and inter-chunk consistency. Second, Grad-CAM identifies only small regions of the referred object, leading to low recall. Therefore, we refine the localization maps with self-attention in Transformer and unsupervised object shape prior. On three popular benchmarks (RefCOCO, RefCOCO+, G-Ref), our method significantly outperforms recent comparable techniques. We also show that our method is applicable to various levels of supervision and obtains better performance than recent methods.

A key problem in multi-label classification is to utilize dependencies among the labels. Chaining classifiers are a simple technique for addressing this problem but current algorithms all assume a fixed, static label ordering. In this work, we propose a multi-label classification approach which allows to choose a dynamic, context-dependent label ordering. Our proposed approach consists of two sub-components: a simple EM-like algorithm which bootstraps the learned model, and a more elaborate approach based on reinforcement learning. Our experiments on three public multi-label classification benchmarks show that our proposed dynamic label ordering approach based on reinforcement learning outperforms recurrent neural networks with fixed label ordering across both bipartition and ranking measures on all the three datasets. As a result, we obtain a powerful sequence prediction-based algorithm for multi-label classification, which is able to efficiently and explicitly exploit label dependencies.

Multi-label classification is the task of predicting a set of labels for a given input instance. Classifier chains are a state-of-the-art method for tackling such problems, which essentially converts this problem into a sequential prediction problem, where the labels are first ordered in an arbitrary fashion, and the task is to predict a sequence of binary values for these labels. In this paper, we replace classifier chains with recurrent neural networks, a sequence-to-sequence prediction algorithm which has recently been successfully applied to sequential prediction tasks in many domains. The key advantage of this approach is that it allows to focus on the prediction of the positive labels only, a much smaller set than the full set of possible labels. Moreover, parameter sharing across all classifiers allows to better exploit information of previous decisions. As both, classifier chains and recurrent neural networks depend on a fixed ordering of the labels, which is typically not part of a multi-label problem specification, we also compare different ways of ordering the label set, and give some recommendations on suitable ordering strategies.

Conventional multi-label classification algorithms treat the target labels of the classification task as mere symbols that are void of an inherent semantics. However, in many cases textual descriptions of these labels are available or can be easily constructed from public document sources such as Wikipedia. In this paper, we investigate an approach for embedding documents and labels into a joint space while sharing word representations between documents and labels. For finding such embeddings, we rely on the text of documents as well as descriptions for the labels. The use of such label descriptions not only lets us expect an increased performance on conventional multi-label text classification tasks, but can also be used to make predictions for labels that have not been seen during the training phase. The potential of our method is demonstrated on the multi-label classification task of assigning keywords from the Medical Subject Headings (MeSH) to publications in biomedical research, both in a conventional and in a zero-shot learning setting.

Traditional machine translation systems often require heavy feature engineering and the combination of multiple techniques for solving different subproblems. In recent years, several end-to-end learning architectures based on recurrent neural networks have been proposed. Unlike traditional systems, Neural Machine Translation (NMT) systems learn the parameters of the model and require only minimal preprocessing. Memory and time constraints allow to take only a fixed number of words into account, which leads to the out-of-vocabulary (OOV) problem. In this work, we analyze why the OOV problem arises and why it is considered a serious problem in German. We study the effectiveness of compound word splitters for alleviating the OOV problem, resulting in a 2.5+ BLEU points improvement over a baseline on the WMT’14 German-to-English translation task. For English-to-German translation, we use target-side compound splitting through a special syntax during training that allows the model to merge compound words and gain 0.2 BLEU points.