Attention mechanism plays a dominant role in the sequence generation models and has been used to improve the performance of machine translation and abstractive text summarization. Different from neural machine translation, in the task of text summarization, salience estimation for words, phrases or sentences is a critical component, since the output summary is a distillation of the input text. Although the typical attention mechanism can conduct text fragment selection from the input text conditioned on the decoder states, there is still a gap to conduct direct and effective salience detection. To bring back direct salience estimation for summarization with neural networks, we propose a Multi-Attention Learning framework which contains two new attention learning components for salience estimation: supervised attention learning and unsupervised attention learning. We regard the attention weights as the salience information, which means that the semantic units with large attention value will be more important. The context information obtained based on the estimated salience is incorporated with the typical attention mechanism in the decoder to conduct summary generation. Extensive experiments on some benchmark datasets in different languages demonstrate the effectiveness of the proposed framework for the task of abstractive summarization.
Named Entity Recognition (NER) is a foundational NLP task that aims to provide class labels like Person, Location, Organisation, Time, and Number to words in free text. Named Entities can also be multi-word expressions where the additional I-O-B annotation information helps label them during the NER annotation process. While English and European languages have considerable annotated data for the NER task, Indian languages lack on that front -- both in terms of quantity and following annotation standards. This paper releases a significantly sized standard-abiding Hindi NER dataset containing 109,146 sentences and 2,220,856 tokens, annotated with 11 tags. We discuss the dataset statistics in all their essential detail and provide an in-depth analysis of the NER tag-set used with our data. The statistics of tag-set in our dataset show a healthy per-tag distribution, especially for prominent classes like Person, Location and Organisation. Since the proof of resource-effectiveness is in building models with the resource and testing the model on benchmark data and against the leader-board entries in shared tasks, we do the same with the aforesaid data. We use different language models to perform the sequence labelling task for NER and show the efficacy of our data by performing a comparative evaluation with models trained on another dataset available for the Hindi NER task. Our dataset helps achieve a weighted F1 score of 88.78 with all the tags and 92.22 when we collapse the tag-set, as discussed in the paper. To the best of our knowledge, no available dataset meets the standards of volume (amount) and variability (diversity), as far as Hindi NER is concerned. We fill this gap through this work, which we hope will significantly help NLP for Hindi. We release this dataset with our code and models at https://github.com/cfiltnlp/HiNER
Most existing vision-language pre-training methods focus on understanding tasks and use BERT-like objectives (masked language modeling and image-text matching) during pretraining. Although they perform well in many understanding downstream tasks, e.g., visual question answering, image-text retrieval and visual entailment, they do not possess the ability to generate. To tackle this problem, we propose Unified multimodal pre-training for both Vision-Language understanding and generation (UniVL). The proposed UniVL is capable of handling both understanding tasks and generative tasks. We augment existing pretraining paradigms that only use random masks with causal masks, i.e., triangular masks that mask out future tokens, such that the pre-trained models can have autoregressive generation abilities by design. We formulate several previous understanding tasks as a text generation task and propose to use prompt-based method for fine-tuning on different downstream tasks. Our experiments show that there is a trade-off between understanding tasks and generation tasks while using the same model, and a feasible way to improve both tasks is to use more data. Our UniVL framework attains comparable performance to recent vision-language pre-training methods on both understanding tasks and generation tasks. Moreover, we demostrate that prompt-based finetuning is more data-efficient - it outperforms discriminative methods in few-shot scenarios.
In this paper, we propose a variational autoencoder with disentanglement priors, VAE-DPRIOR, for conditional natural language generation with none or a handful of task-specific labeled examples. In order to improve compositional generalization, our model performs disentangled representation learning by introducing a prior for the latent content space and another prior for the latent label space. We show both empirically and theoretically that the conditional priors can already disentangle representations even without specific regularizations as in the prior work. We can also sample diverse content representations from the content space without accessing data of the seen tasks, and fuse them with the representations of novel tasks for generating diverse texts in the low-resource settings. Our extensive experiments demonstrate the superior performance of our model over competitive baselines in terms of i) data augmentation in continuous zero/few-shot learning, and ii) text style transfer in both zero/few-shot settings.
End-to-end text-to-speech synthesis (TTS), which generates speech sounds directly from strings of texts or phonemes, has improved the quality of speech synthesis over the conventional TTS. However, most previous studies have been evaluated based on subjective naturalness and have not objectively examined whether they can reproduce pitch patterns of phonological phenomena such as downstep, rhythmic boost, and initial lowering that reflect syntactic structures in Japanese. These phenomena can be linguistically explained by phonological constraints and the syntax$\unicode{x2013}$prosody mapping hypothesis (SPMH), which assumes projections from syntactic structures to phonological hierarchy. Although some experiments in psycholinguistics have verified the validity of the SPMH, it is crucial to investigate whether it can be implemented in TTS. To synthesize linguistic phenomena involving syntactic or phonological constraints, we propose a model using phonological symbols based on the SPMH and prosodic well-formedness constraints. Experimental results showed that the proposed method synthesized similar pitch patterns to those reported in linguistics experiments for the phenomena of initial lowering and rhythmic boost. The proposed model efficiently synthesizes phonological phenomena in the test data that were not explicitly included in the training data.
The dominant approaches to text representation in natural language rely on learning embeddings on massive corpora which have convenient properties such as compositionality and distance preservation. In this paper, we develop a novel method to learn a heavy-tailed embedding with desirable regularity properties regarding the distributional tails, which allows to analyze the points far away from the distribution bulk using the framework of multivariate extreme value theory. In particular, a classifier dedicated to the tails of the proposed embedding is obtained which performance outperforms the baseline. This classifier exhibits a scale invariance property which we leverage by introducing a novel text generation method for label preserving dataset augmentation. Numerical experiments on synthetic and real text data demonstrate the relevance of the proposed framework and confirm that this method generates meaningful sentences with controllable attribute, e.g. positive or negative sentiment.
Keyphrase extraction aims at automatically extracting a list of "important" phrases which represent the key concepts in a document. Prior approaches for unsupervised keyphrase extraction resort to heuristic notions of phrase importance via embedding similarities or graph centrality, requiring extensive domain expertise to develop them. Our work proposes an alternative operational definition: phrases that are most useful for predicting the topic of a text are important keyphrases. To this end, we propose INSPECT -- a self-explaining neural framework for identifying influential keyphrases by measuring the predictive impact of input phrases on the downstream task of topic classification. We show that this novel approach not only alleviates the need for ad-hoc heuristics but also achieves state-of-the-art results in unsupervised keyphrase extraction across four diverse datasets in two domains: scientific publications and news articles. Ultimately, our study suggests a new usage of interpretable neural networks as an intrinsic component in NLP systems, and not only as a tool for explaining model predictions to humans.
Recent rapid advancements in deep pre-trained language models and the introductions of large datasets have powered research in embedding-based dense retrieval. While several good research papers have emerged, many of them come with their own software stacks. These stacks are typically optimized for some particular research goals instead of efficiency or code structure. In this paper, we present Tevatron, a dense retrieval toolkit optimized for efficiency, flexibility, and code simplicity. Tevatron provides a standardized pipeline for dense retrieval including text processing, model training, corpus/query encoding, and search. This paper presents an overview of Tevatron and demonstrates its effectiveness and efficiency across several IR and QA data sets. We also show how Tevatron's flexible design enables easy generalization across datasets, model architectures, and accelerator platforms(GPU/TPU). We believe Tevatron can serve as an effective software foundation for dense retrieval system research including design, modeling, and optimization.
In this paper, we use a large-scale play scripts dataset to propose the novel task of theatrical cue generation from dialogues. Using over one million lines of dialogue and cues, we approach the problem of cue generation as a controlled text generation task, and show how cues can be used to enhance the impact of dialogue using a language model conditioned on a dialogue/cue discriminator. In addition, we explore the use of topic keywords and emotions for controlled text generation. Extensive quantitative and qualitative experiments show that language models can be successfully used to generate plausible and attribute-controlled texts in highly specialised domains such as play scripts. Supporting materials can be found at: https://catlab-team.github.io/cuegen.
Automatic speaker verification is susceptible to various manipulations and spoofing, such as text-to-speech (TTS) synthesis, voice conversion (VC), replay, tampering, and so on. In this paper, we consider a new spoofing scenario called "Partial Spoof" (PS) in which synthesized or transformed audio segments are embedded into a bona fide speech utterance. While existing countermeasures (CMs) can detect fully spoofed utterances, there is a need for their adaptation or extension to the PS scenario to detect utterances in which only a part of the audio signal is generated and hence only a fraction of an utterance is spoofed. For improved explainability, such new CMs should ideally also be able to detect such short spoofed segments. Our previous study introduced the first version of a speech database suitable for training CMs for the PS scenario and showed that, although it is possible to train CMs to execute the two types of detection described above, there is much room for improvement. In this paper we propose various improvements to construct a significantly more accurate CM that can detect short generated spoofed audio segments at finer temporal resolutions. First, we introduce newly proposed self-supervised pre-trained models as enhanced feature extractors. Second, we extend the PartialSpoof database by adding segment labels for various temporal resolutions, ranging from 20 ms to 640 ms. Third, we propose a new CM and training strategies that enable the simultaneous use of the utterance-level and segment-level labels at different temporal resolutions. We also show that the proposed CM is capable of detecting spoofing at the utterance level with low error rates, not only in the PS scenario but also in a related logical access (LA) scenario. The equal error rates of utterance-level detection on the PartialSpoof and the ASVspoof 2019 LA database were 0.47% and 0.59%, respectively.