Topic modeling often requires examining topics from multiple perspectives to uncover hidden patterns, especially in less explored areas. This paper presents an approach to address this need, utilizing weighted keywords from various aspects derived from a domain knowledge. The research method starts with standard topic modeling. Then, it adds a process consisting of four key steps. First, it defines keywords for each aspect. Second, it gives weights to these keywords based on their relevance. Third, it calculates relevance scores for aspect-weighted keywords and topic keywords to create aspect-topic models. Fourth, it uses these scores to tune relevant new documents. Finally, the generated topic models are interpreted and validated. The findings show that top-scoring documents are more likely to be about the same aspect of a topic. This highlights the model's effectiveness in finding the related documents to the aspects.

The reviewers reached, after discussion, the consensus that this paper presenting a novel way of modelling strucutured memory is worth including in the conference. The modelling aspect of the paper was of interest to the reviewers, who were furthermore reasonably confident that the method has empirical merit thanks to the experiments both synthetic and "real world". Perhaps the main weakness of this paper is that while the synthetic experiments prove the concepts and the sentiment analysis experiments show robustness to noisy data, further non-synthetic experiments might have further showcased applications of this method to tasks which the community cares about. For now, I find it of a sufficient standard for publication, and anticipate that further work will demonstrate whether this method stands well against other tasks... or not.

Detecting factual inconsistency for long document summarization remains challenging, given the complex structure of the source article and long summary length. In this work, we study factual inconsistency errors and connect them with a line of discourse analysis. We find that errors are more common in complex sentences and are associated with several discourse features. We propose a framework that decomposes long texts into discourse-inspired chunks and utilizes discourse information to better aggregate sentence-level scores predicted by natural language inference models. Our approach shows improved performance on top of different model baselines over several evaluation benchmarks, covering rich domains of texts, focusing on long document summarization. This underscores the significance of incorporating discourse features in developing models for scoring summaries for long document factual inconsistency.

We develop a fast algorithm for the Admixture of Poisson MRFs (APM) topic model [1] and propose a novel metric to directly evaluate this model. The APM topic model recently introduced by Inouye et al. [1] is the first topic model that allows for word dependencies within each topic unlike in previous topic models like LDA that assume independence between words within a topic. Research in both the semantic coherence of a topic models [2, 3, 4, 5] and measures of model fitness [6] provide strong support that explicitly modeling word dependencies--as in APM--could be both semantically meaningful and essential for appropriately modeling real text data.

Topic models, such as Latent Dirichlet Allocation (LDA), posit that documents are drawn from admixtures of distributions over words, known as topics. The inference problem of recovering topics from such a collection of documents drawn from admixtures, is NP-hard. Making a strong assumption called separability, [4] gave the first provable algorithm for inference. For the widely used LDA model, [6] gave a provable algorithm using clever tensor-methods.

While topic models can discover patterns of word usage in large corpora, it is difficult to meld this unsupervised structure with noisy, human-provided labels, especially when the label space is large.

Latent Dirichlet allocation (LDA) is a popular generative model of various objects such as texts and images, where an object is expressed as a mixture of latent topics. In this paper, we theoretically investigate variational Bayesian (VB) learning in LDA. More specifically, we analytically derive the leading term of the VB free energy under an asymptotic setup, and show that there exist transition thresholds in Dirichlet hyperparameters around which the sparsity-inducing behavior drastically changes. Then we further theoretically reveal the notable phenomenon that VB tends to induce weaker sparsity than MAP in the LDA model, which is opposed to other models. We experimentally demonstrate the practical validity of our asymptotic theory on real-world Last.FM music data.

Supervised topic models simultaneously model the latent topic structure of large collections of documents and a response variable associated with each document. Existing inference methods are based on either variational approximation or Monte Carlo sampling. This paper presents a novel spectral decomposition algorithm to recover the parameters of supervised latent Dirichlet allocation (sLDA) models. The Spectral-sLDA algorithm is provably correct and computationally efficient. We prove a sample complexity bound and subsequently derive a sufficient condition for the identifiability of sLDA. Thorough experiments on a diverse range of synthetic and real-world datasets verify the theory and demonstrate the practical effectiveness of the algorithm.

The beta-negative binomial process (BNBP), an integer-valued stochastic process, is employed to partition a count vector into a latent random count matrix. As the marginal probability distribution of the BNBP that governs the exchangeable random partitions of grouped data has not yet been developed, current inference for the BNBP has to truncate the number of atoms of the beta process. This paper introduces an exchangeable partition probability function to explicitly describe how the BNBP clusters the data points of each group into a random number of exchangeable partitions, which are shared across all the groups. A fully collapsed Gibbs sampler is developed for the BNBP, leading to a novel nonparametric Bayesian topic model that is distinct from existing ones, with simple implementation, fast convergence, good mixing, and state-of-the-art predictive performance.

Weaknesses: The main problem with the paper is the game design. In visual dialogue, i.e GuessWhich game[2], does not have access to the image. It has to build up the visual representation based on the caption and dialogue. That is why having a caption is important for the GuessWhich game (L69). While in the proposed game, since Q-Bot has constant access to the images. It just needs to ask questions such that it distinguished the one image from the other.