Reinforced Video Captioning with Entailment Rewards

Sequence-to-sequence models have shown promising improvements on the temporal task of video captioning, but they optimize word-level cross-entropy loss during training. First, using policy gradient and mixed-loss methods for reinforcement learning, we directly optimize sentence-level task-based metrics (as rewards), achieving significant improvements over the baseline, based on both automatic metrics and human evaluation on multiple datasets. Next, we propose a novel entailment-enhanced reward (CIDEnt) that corrects phrase-matching based metrics (such as CIDEr) to only allow for logically-implied partial matches and avoid contradictions, achieving further significant improvements over the CIDEr-reward model. Overall, our CIDEnt-reward model achieves the new state-of-the-art on the MSR-VTT dataset.Comment: EMNLP 2017 (9 pages

Ferromagnetism within the periodic Anderson model: A new approximation scheme

We introduce a new approach to the periodic Anderson model (PAM) that allows a detailed investigation of the magnetic properties in the Kondo as well as the intermediate valence regime. Our method is based on an exact mapping of the PAM onto an effective medium strong-coupling Hubbard model. For the latter, the so-called spectral density approach (SDA) is rather well motivated since it is based on exact results in the strong coupling limit. Besides the T=0 phase diagram, magnetization curves and Curie temperatures are presented and discussed with help of temperature-dependent quasiparticle densities of state. In the intermediate valence regime, the hybridization gap plays a major role in determining the magnetic behaviour. Furthermore, our results indicate that ferromagnetism in this parameter regime is not induced by an effective spin-spin interaction between the localized levels mediated by conduction electrons as it is the case in the Kondo regime. The magnetic ordering is rather a single band effect within an effective f-band.Comment: 13 pages, 16 figures, Phys. Stat. Sol. in pres

Low density approach to the Kondo-lattice model

We propose a new approach to the (ferromagnetic) Kondo-lattice model in the low density region, where the model is thought to give a reasonable frame work for manganites with perovskite structure exhibiting the "colossal magnetoresistance" -effect. Results for the temperature- dependent quasiparticle density of states are presented. Typical features can be interpreted in terms of elementary spin-exchange processes between itinerant conduction electrons and localized moments. The approach is exact in the zero bandwidth limit for all temperatures and at T=0 for arbitrary bandwidths, fulfills exact high-energy expansions and reproduces correctly second order perturbation theory in the exchange coupling.Comment: 11 pages, 7 figures, accepted by PR

Multi-Task Video Captioning with Video and Entailment Generation

Video captioning, the task of describing the content of a video, has seen some promising improvements in recent years with sequence-to-sequence models, but accurately learning the temporal and logical dynamics involved in the task still remains a challenge, especially given the lack of sufficient annotated data. We improve video captioning by sharing knowledge with two related directed-generation tasks: a temporally-directed unsupervised video prediction task to learn richer context-aware video encoder representations, and a logically-directed language entailment generation task to learn better video-entailed caption decoder representations. For this, we present a many-to-many multi-task learning model that shares parameters across the encoders and decoders of the three tasks. We achieve significant improvements and the new state-of-the-art on several standard video captioning datasets using diverse automatic and human evaluations. We also show mutual multi-task improvements on the entailment generation task.Comment: ACL 2017 (14 pages w/ supplementary