Understanding movement described in text documents is important since text descriptions of movement contain a wealth of geographic and contextual information about the movement of people, wildlife, goods, and much more. Our research makes several contributions to improve our understanding of movement descriptions in text. First, we show how interpreting geographic movement described in text is challenging because of general spatial terms, linguistic constructions that make the thing(s) moving unclear, and many types of temporal references and groupings, among others. Next, as a step to overcome these challenges, we report on an experiment with human subjects through which we identify multiple important characteristics of movement descriptions (found in text) that humans use to differentiate one movement description from another. Based on our empirical results, we provide recommendations for computational analysis using movement described in text documents. Our findings contribute towards an improved understanding of the important characteristics of the underused information about geographic movement that is in the form of text descriptions.
Text generation is of great importance to many natural language processing applications. However, maximization-based decoding methods (e.g. beam search) of neural language models often lead to degenerate solutions -- the generated text is unnatural and contains undesirable repetitions. Existing approaches introduce stochasticity via sampling or modify training objectives to decrease probabilities of certain tokens (e.g., unlikelihood training). However, they often lead to solutions that lack coherence. In this work, we show that an underlying reason for model degeneration is the anisotropic distribution of token representations. We present a contrastive solution: (i) SimCTG, a contrastive training objective to calibrate the model's representation space, and (ii) a decoding method -- contrastive search -- to encourage diversity while maintaining coherence in the generated text. Extensive experiments and analyses on three benchmarks from two languages demonstrate that our proposed approach outperforms state-of-the-art text generation methods as evaluated by both human and automatic metrics.
This paper surveys vision-language pre-training (VLP) methods for multimodal intelligence that have been developed in the last few years. We group these approaches into three categories: ($i$) VLP for image-text tasks, such as image captioning, image-text retrieval, visual question answering, and visual grounding; ($ii$) VLP for core computer vision tasks, such as (open-set) image classification, object detection, and segmentation; and ($iii$) VLP for video-text tasks, such as video captioning, video-text retrieval, and video question answering. For each category, we present a comprehensive review of state-of-the-art methods, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies. In addition, for each category, we discuss advanced topics being actively explored in the research community, such as big foundation models, unified modeling, in-context few-shot learning, knowledge, robustness, and computer vision in the wild, to name a few.
Graph Convolutional Networks (GCN) have achieved state-of-art results on single text classification tasks like sentiment analysis, emotion detection, etc. However, the performance is achieved by testing and reporting on resource-rich languages like English. Applying GCN for multi-task text classification is an unexplored area. Moreover, training a GCN or adopting an English GCN for Indian languages is often limited by data availability, rich morphological variation, syntax, and semantic differences. In this paper, we study the use of GCN for the Telugu language in single and multi-task settings for four natural language processing (NLP) tasks, viz. sentiment analysis (SA), emotion identification (EI), hate-speech (HS), and sarcasm detection (SAR). In order to evaluate the performance of GCN with one of the Indian languages, Telugu, we analyze the GCN based models with extensive experiments on four downstream tasks. In addition, we created an annotated Telugu dataset, TEL-NLP, for the four NLP tasks. Further, we propose a supervised graph reconstruction method, Multi-Task Text GCN (MT-Text GCN) on the Telugu that leverages to simultaneously (i) learn the low-dimensional word and sentence graph embeddings from word-sentence graph reconstruction using graph autoencoder (GAE) and (ii) perform multi-task text classification using these latent sentence graph embeddings. We argue that our proposed MT-Text GCN achieves significant improvements on TEL-NLP over existing Telugu pretrained word embeddings, and multilingual pretrained Transformer models: mBERT, and XLM-R. On TEL-NLP, we achieve a high F1-score for four NLP tasks: SA (0.84), EI (0.55), HS (0.83) and SAR (0.66). Finally, we show our model's quantitative and qualitative analysis on the four NLP tasks in Telugu.
Recent work shows that sensitive user data can be reconstructed from gradient updates, breaking the key privacy promise of federated learning. While success was demonstrated primarily on image data, these methods do not directly transfer to other domains such as text. In this work, we propose LAMP, a novel attack tailored to textual data, that successfully reconstructs original text from gradients. Our key insight is to model the prior probability of the text with an auxiliary language model, utilizing it to guide the search towards more natural text. Concretely, LAMP introduces a discrete text transformation procedure that minimizes both the reconstruction loss and the prior text probability, as provided by the auxiliary language model. The procedure is alternated with a continuous optimization of the reconstruction loss, which also regularizes the length of the reconstructed embeddings. Our experiments demonstrate that LAMP reconstructs the original text significantly more precisely than prior work: we recover 5x more bigrams and $23\%$ longer subsequences on average. Moreover, we are first to recover inputs from batch sizes larger than 1 for textual models. These findings indicate that gradient updates of models operating on textual data leak more information than previously thought.
Large-scale multi-modal training with image-text pairs imparts strong generalization to CLIP model. Since training on a similar scale for videos is infeasible, recent approaches focus on the effective transfer of image-based CLIP to the video domain. In this pursuit, new parametric modules are added to learn temporal information and inter-frame relationships which require meticulous design efforts. Furthermore, when the resulting models are learned on videos, they tend to overfit on the given task distribution and lack in generalization aspect. This begs the following question: How to effectively transfer image-level CLIP representations to videos? In this work, we show that a simple Video Fine-tuned CLIP (ViFi-CLIP) baseline is generally sufficient to bridge the domain gap from images to videos. Our qualitative analysis illustrates that the frame-level processing from CLIP image-encoder followed by feature pooling and similarity matching with corresponding text embeddings helps in implicitly modeling the temporal cues within ViFi-CLIP. Such fine-tuning helps the model to focus on scene dynamics, moving objects and inter-object relationships. For low-data regimes where full fine-tuning is not viable, we propose a `bridge and prompt' approach that first uses fine-tuning to bridge the domain gap and then learns prompts on language and vision side to adapt CLIP representations. We extensively evaluate this simple yet strong baseline on zero-shot, base-to-novel generalization, few-shot and fully supervised settings across five video benchmarks. Our code is available at https://github.com/muzairkhattak/ViFi-CLIP.
How people think, feel, and behave, primarily is a representation of their personality characteristics. By being conscious of personality characteristics of individuals whom we are dealing with or decided to deal with, one can competently ameliorate the relationship, regardless of its type. With the rise of Internet-based communication infrastructures (social networks, forums, etc.), a considerable amount of human communications take place there. The most prominent tool in such communications, is the language in written and spoken form that adroitly encodes all those essential personality characteristics of individuals. Text-based Automatic Personality Prediction (APP) is the automated forecasting of the personality of individuals based on the generated/exchanged text contents. This paper presents a novel knowledge graph-enabled approach to text-based APP that relies on the Big Five personality traits. To this end, given a text a knowledge graph which is a set of interlinked descriptions of concepts, was built through matching the input text's concepts with DBpedia knowledge base entries. Then, due to achieving more powerful representation the graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon, and MRC psycholinguistic database information. Afterwards, the knowledge graph which is now a knowledgeable alternative for the input text was embedded to yield an embedding matrix. Finally, to perform personality predictions the resulting embedding matrix was fed to four suggested deep learning models independently, which are based on convolutional neural network (CNN), simple recurrent neural network (RNN), long short term memory (LSTM) and bidirectional long short term memory (BiLSTM). The results indicated a considerable improvements in prediction accuracies in all of the suggested classifiers.
Translating spoken languages into Sign languages is necessary for open communication between the hearing and hearing-impaired communities. To achieve this goal, we propose the first method for animating a text written in HamNoSys, a lexical Sign language notation, into signed pose sequences. As HamNoSys is universal, our proposed method offers a generic solution invariant to the target Sign language. Our method gradually generates pose predictions using transformer encoders that create meaningful representations of the text and poses while considering their spatial and temporal information. We use weak supervision for the training process and show that our method succeeds in learning from partial and inaccurate data. Additionally, we offer a new distance measurement for pose sequences, normalized Dynamic Time Warping (nDTW), based on DTW over normalized keypoints trajectories, and validate its correctness using AUTSL, a large-scale Sign language dataset. We show that it measures the distance between pose sequences more accurately than existing measurements and use it to assess the quality of our generated pose sequences. Code for the data pre-processing, the model, and the distance measurement is publicly released for future research.
Spontaneous speech has many affective and pragmatic functions that are interesting and challenging to model in TTS (text-to-speech). However, the presence of reduced articulation, fillers, repetitions, and other disfluencies mean that text and acoustics are less well aligned than in read speech. This is problematic for attention-based TTS. We propose a TTS architecture that is particularly suited for rapidly learning to speak from irregular and small datasets while also reproducing the diversity of expressive phenomena present in spontaneous speech. Specifically, we modify an existing neural HMM-based TTS system, which is capable of stable, monotonic alignments for spontaneous speech, and add utterance-level prosody control, so that the system can represent the wide range of natural variability in a spontaneous speech corpus. We objectively evaluate control accuracy and perform a subjective listening test to compare to a system without prosody control. To exemplify the power of combining mid-level prosody control and ecologically valid data for reproducing intricate spontaneous speech phenomena, we evaluate the system's capability of synthesizing two types of creaky phonation. Audio samples are available at https://hfkml.github.io/pc_nhmm_tts/
The consumption of news has changed significantly as the Web has become the most influential medium for information. To analyze and contextualize the large amount of news published every day, the geographic focus of an article is an important aspect in order to enable content-based news retrieval. There are methods and datasets for geolocation estimation from text or photos, but they are typically considered as separate tasks. However, the photo might lack geographical cues and text can include multiple locations, making it challenging to recognize the focus location using a single modality. In this paper, a novel dataset called Multimodal Focus Location of News (MM-Locate-News) is introduced. We evaluate state-of-the-art methods on the new benchmark dataset and suggest novel models to predict the focus location of news using both textual and image content. The experimental results show that the multimodal model outperforms unimodal models.