561 research outputs found
Concept-to-text Generation via Discriminative Reranking
This paper proposes a data-driven method for concept-to-text generation, the task of automatically producing textual output from non-linguistic input. A key insight in our approach is to reduce the tasks of content selection (“what to say”) and surface realization (“how to say”) into a common parsing problem. We define a probabilistic context-free grammar that describes the structure of the input (a corpus of database records and text describing some of them) and represent it compactly as a weighted hypergraph. The hypergraph structure encodes exponentially many derivations, which we rerank discriminatively using local and global features. We propose a novel decoding algorithm for finding the best scoring derivation and generating in this setting. Experimental evaluation on the ATIS domain shows that our model outperforms a competitive discriminative system both using BLEU and in a judgment elicitation study.
Strategies for Searching Video Content with Text Queries or Video Examples
The large number of user-generated videos uploaded on to the Internet
everyday has led to many commercial video search engines, which mainly rely on
text metadata for search. However, metadata is often lacking for user-generated
videos, thus these videos are unsearchable by current search engines.
Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity
problem by directly analyzing the visual and audio streams of each video. CBVR
encompasses multiple research topics, including low-level feature design,
feature fusion, semantic detector training and video search/reranking. We
present novel strategies in these topics to enhance CBVR in both accuracy and
speed under different query inputs, including pure textual queries and query by
video examples. Our proposed strategies have been incorporated into our
submission for the TRECVID 2014 Multimedia Event Detection evaluation, where
our system outperformed other submissions in both text queries and video
example queries, thus demonstrating the effectiveness of our proposed
approaches
Quality Control at Your Fingertips: Quality-Aware Translation Models
Maximum-a-posteriori (MAP) decoding is the most widely used decoding strategy
for neural machine translation (NMT) models. The underlying assumption is that
model probability correlates well with human judgment, with better translations
being more likely. However, research has shown that this assumption does not
always hold, and decoding strategies which directly optimize a utility
function, like Minimum Bayes Risk (MBR) or Quality-Aware decoding can
significantly improve translation quality over standard MAP decoding. The main
disadvantage of these methods is that they require an additional model to
predict the utility, and additional steps during decoding, which makes the
entire process computationally demanding. In this paper, we propose to make the
NMT models themselves quality-aware by training them to estimate the quality of
their own output. During decoding, we can use the model's own quality estimates
to guide the generation process and produce the highest-quality translations
possible. We demonstrate that the model can self-evaluate its own output during
translation, eliminating the need for a separate quality estimation model.
Moreover, we show that using this quality signal as a prompt during MAP
decoding can significantly improve translation quality. When using the internal
quality estimate to prune the hypothesis space during MBR decoding, we can not
only further improve translation quality, but also reduce inference speed by
two orders of magnitude
Context-aware Captions from Context-agnostic Supervision
We introduce an inference technique to produce discriminative context-aware
image captions (captions that describe differences between images or visual
concepts) using only generic context-agnostic training data (captions that
describe a concept or an image in isolation). For example, given images and
captions of "siamese cat" and "tiger cat", we generate language that describes
the "siamese cat" in a way that distinguishes it from "tiger cat". Our key
novelty is that we show how to do joint inference over a language model that is
context-agnostic and a listener which distinguishes closely-related concepts.
We first apply our technique to a justification task, namely to describe why an
image contains a particular fine-grained category as opposed to another
closely-related category of the CUB-200-2011 dataset. We then study
discriminative image captioning to generate language that uniquely refers to
one of two semantically-similar images in the COCO dataset. Evaluations with
discriminative ground truth for justification and human studies for
discriminative image captioning reveal that our approach outperforms baseline
generative and speaker-listener approaches for discrimination.Comment: Accepted to CVPR 2017 (Spotlight
Automatic Figure Ranking and User Interfacing for Intelligent Figure Search
Figures are important experimental results that are typically reported in full-text bioscience articles. Bioscience researchers need to access figures to validate research facts and to formulate or to test novel research hypotheses. On the other hand, the sheer volume of bioscience literature has made it difficult to access figures. Therefore, we are developing an intelligent figure search engine (http://figuresearch.askhermes.org). Existing research in figure search treats each figure equally, but we introduce a novel concept of "figure ranking": figures appearing in a full-text biomedical article can be ranked by their contribution to the knowledge discovery.We empirically validated the hypothesis of figure ranking with over 100 bioscience researchers, and then developed unsupervised natural language processing (NLP) approaches to automatically rank figures. Evaluating on a collection of 202 full-text articles in which authors have ranked the figures based on importance, our best system achieved a weighted error rate of 0.2, which is significantly better than several other baseline systems we explored. We further explored a user interfacing application in which we built novel user interfaces (UIs) incorporating figure ranking, allowing bioscience researchers to efficiently access important figures. Our evaluation results show that 92% of the bioscience researchers prefer as the top two choices the user interfaces in which the most important figures are enlarged. With our automatic figure ranking NLP system, bioscience researchers preferred the UIs in which the most important figures were predicted by our NLP system than the UIs in which the most important figures were randomly assigned. In addition, our results show that there was no statistical difference in bioscience researchers' preference in the UIs generated by automatic figure ranking and UIs by human ranking annotation.The evaluation results conclude that automatic figure ranking and user interfacing as we reported in this study can be fully implemented in online publishing. The novel user interface integrated with the automatic figure ranking system provides a more efficient and robust way to access scientific information in the biomedical domain, which will further enhance our existing figure search engine to better facilitate accessing figures of interest for bioscientists
Aligning Neural Machine Translation Models: Human Feedback in Training and Inference
Reinforcement learning from human feedback (RLHF) is a recent technique to
improve the quality of the text generated by a language model, making it closer
to what humans would generate. A core ingredient in RLHF's success in aligning
and improving large language models (LLMs) is its reward model, trained using
human feedback on model outputs. In machine translation (MT), where metrics
trained from human annotations can readily be used as reward models, recent
methods using minimum Bayes risk decoding and reranking have succeeded in
improving the final quality of translation. In this study, we comprehensively
explore and compare techniques for integrating quality metrics as reward models
into the MT pipeline. This includes using the reward model for data filtering,
during the training phase through RL, and at inference time by employing
reranking techniques, and we assess the effects of combining these in a unified
approach. Our experimental results, conducted across multiple translation
tasks, underscore the crucial role of effective data filtering, based on
estimated quality, in harnessing the full potential of RL in enhancing MT
quality. Furthermore, our findings demonstrate the effectiveness of combining
RL training with reranking techniques, showcasing substantial improvements in
translation quality.Comment: 14 pages, work-in-progres
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