Novel object captioning aims at describing objects absent from training data, with the key ingredient being the provision of object vocabulary to the model. Although existing methods heavily rely on an object detection model, we view the detection step as vocabulary retrieval from an external knowledge in the form of embeddings for any object's definition from Wiktionary, where we use in the retrieval image region features learned from a transformers model. We propose an end-to-end Novel Object Captioning with Retrieved vocabulary from External Knowledge method (NOC-REK), which simultaneously learns vocabulary retrieval and caption generation, successfully describing novel objects outside of the training dataset. Furthermore, our model eliminates the requirement for model retraining by simply updating the external knowledge whenever a novel object appears. Our comprehensive experiments on held-out COCO and Nocaps datasets show that our NOCREK is considerably effective against SOTAs.

Story visualization advances the traditional text-to-image generation by enabling multiple image generation based on a complete story.This task requires machines to 1) understand long text inputs and 2) produce a globally consistent image sequence that illustrates the contents of the story.A key challenge of consistent story visualization is to preserve characters that are essential in stories.To tackle the challenge, we propose to adapt a recent work that augments Vector-Quantized Variational Autoencoders (VQ-VAE) with a text-tovisual-token (transformer) architecture.Specifically, we modify the text-to-visual-token module with a two-stage framework: 1) character token planning model that predicts the visual tokens for characters only; 2) visual token completion model that generates the remaining visual token sequence, which is sent to VQ-VAE for finalizing image generations.To encourage characters to appear in the images, we further train the two-stage framework with a character-token alignment objective.Extensive experiments and evaluations demonstrate that the proposed method excels at preserving characters and can produce higher quality image sequences compared with the strong baselines.Code can be found in https: //github.com/PlusLabNLP/VP-CSV

Existing automatic story evaluation methods place a premium on story lexical level coherence, deviating from human preference.We go beyond this limitation by considering a novel Story Evaluation method that mimics human preference when judging a story, namely StoryER, which consists of three sub-tasks: Ranking, Rating and Reasoning.Given either a machine-generated or a human-written story, StoryER requires the machine to output 1) a preference score that corresponds to human preference, 2) specific ratings and their corresponding confidences and 3) comments for various aspects (e.g., opening, character-shaping).To support these tasks, we introduce a wellannotated dataset comprising (i) 100k ranked story pairs; and (ii) a set of 46k ratings and comments on various aspects of the story.We finetune Longformer-Encoder-Decoder (LED) on the collected dataset, with the encoder responsible for preference score and aspect prediction and the decoder for comment generation.Our comprehensive experiments result in a competitive benchmark for each task, showing the high correlation to human preference.In addition, we have witnessed the joint learning of the preference scores, the aspect ratings, and the comments brings gain in each single task.Our dataset and benchmarks are publicly available to advance the research of story evaluation tasks. 1

Stories or narratives are comprised of a sequence of events.To compose interesting stories, professional writers often leverage a creative writing technique called flashback that inserts past events into current storylines as we commonly observe in novels and plays.However, it is challenging for machines to generate flashbacks as it requires solid understanding of event temporal order (e.g.feeling hungry before eat, not vice versa), and the creativity to arrange storylines so that earlier events do not always appear first in narrative order.Two major issues in existing systems exacerbate the challenges: 1) temporal bias in pretraining and story datasets that leads to monotonic event temporal orders; 2) lack of explicit guidance that helps machines decide where to insert flashbacks.We propose to address these issues using structured storylines to encode events and their pair-wise temporal relations ( before , after and vague ) as temporal prompts that guide how stories should unfold temporally.We leverage a Plan-and-Write framework enhanced by reinforcement learning to generate storylines and stories end-toend.Evaluation results show that the proposed method can generate more interesting stories with flashbacks while maintaining textual diversity, fluency and temporal coherence.1

Visual storytelling is a task of generating relevant and interesting stories for given image sequences. In this work we aim at increasing the diversity of the generated stories while preserving the informative content from the images. We propose to foster the diversity and informativeness of a generated story by using a concept selection module that suggests a set of concept candidates. Then, we utilize a large scale pre-trained model to convert concepts and images into full stories. To enrich the candidate concepts, a commonsense knowledge graph is created for each image sequence from which the concept candidates are proposed. To obtain appropriate concepts from the graph, we propose two novel modules that consider the correlation among candidate concepts and the image-concept correlation. Extensive automatic and human evaluation results demonstrate that our model can produce reasonable concepts. This enables our model to outperform the previous models by a large margin on the diversity and informativeness of the story, while retaining the relevance of the story to the image sequence.

Story generation is a task that aims to automatically generate a meaningful story.This task is challenging because it requires high-level understanding of the semantic meaning of sentences and causality of story events.Naive sequence-to-sequence models generally fail to acquire such knowledge, as it is difficult to guarantee logical correctness in a text generation model without strategic planning.In this study, we focus on planning a sequence of events assisted by event graphs and use the events to guide the generator.Rather than using a sequence-to-sequence model to output a sequence, as in some existing works, we propose to generate an event sequence by walking on an event graph.The event graphs are built automatically based on the corpus.To evaluate the proposed approach, we incorporate human participation, both in event planning and story generation.Based on the largescale human annotation results, our proposed approach has been shown to provide more logically correct event sequences and stories compared with previous approaches.

Object recognition ability is indispensable for robots to act like humans in a home environment. For example, when considering an object searching task, humans can recognize a naturally arranged object previously held in their hands while ignoring never observed objects. Even in such a simple task, we need to deal with three complex problems: domain adaptation, open-set recognition, and continual learning. However, most existing datasets are simplified to focus on one problem and do not measure the object recognition ability for home robots when multiple problems are simultaneously present. In this paper, we propose the COSDA-HR (Continual Open Set Domain Adaptation for Home Robot) dataset that requires dealing with the above three problems simultaneously. The COSDA-HR dataset focuses particularly on the scenario in which naturally arranged objects in a room are recognized by training with handheld objects towards the goal of creating a user-friendly teaching system for home robots. We provide various baselines to address the problems in the COSDA-HR dataset by combining state-of-the-art methods from each research area and analyze the limitations of such simple combinations. We consider that it is necessary to study the methods of handling multiple problems simultaneously instead of solving each problem to realize practical object recognition systems for home robots.