For HDMM, weconsiderworkloadsofkrandom 3-waymarginals, fork = 1, 2, 4, 8,..., 256, 455, runfivetrials, andreportrootmeansquarederror, theobjectivefunctionthat HDMMoptimizes. Figure 3ashowstheresults.

The knowledge seeking procedure described in Section 2.1 applies a search algorithm over the graph Each of such queries takes constant time. As mentioned in Section 2.3, the approach described in this paper can be used to answer any valid We proceed by induction on the number of literals |Q |. 3 Base case. For the experiments on KBQA, we assume that we only have access to pairs of questions and answers, i.e. the actual inferential chain leading from the question to the answer is latent. Therefore, we resort to weak supervision to train the model. Inspired by such insight, we employ a similar technique to enhance the performance of our model.

This 14th century work is divided into 3 sections: "Inferno", "Purgatorio" & "Paradiso" (y) Barack Obama was born in Hawaii.(x) Define "middle ear"(x) Question Answering: Question Query The middle ear includes the tympanic cavity and the three ossicles.

A.1 Motivation For what purpose was the dataset created? As an affiliated dataset, we created MIMIC-CXR-VQA to provide a benchmark for medical visual question answering systems. Who created the dataset (e.g., which team, research group) and on behalf of which Who funded the creation of the dataset? This work was (partially) supported by Microsoft Research Asia, Institute of Information & Communications Technology Planning & Evaluation (IITP) grant (No.2019-0-00075, RS-2022-00155958), National Research Foundation of Korea (NRF) grant (NRF-2020H1D3A2A03100945), and the Korea Health Industry Development Institute (KHIDI) What do the instances that comprise the dataset represent (e.g., documents, photos, EHRXQA contains natural questions and corresponding SQL/NeuralSQL queries (text). How many instances are there in total (of each type, if appropriate)? In EHRXQA, there are about 46.2K instances (16,366 image-related samples, 16,529 table-related samples, and 13,257 image+table-related samples).

Causal video question answering (QA) has garnered increasing interest, yet existing datasets often lack depth in causal reasoning. To address this gap, we capitalize on the unique properties of cartoons and construct CausalChaos!, a novel, challenging causal Why-QA dataset built upon the iconic Tom and Jerry cartoon series. Cartoons use the principles of animation that allow animators to create expressive, unambiguous causal relationships between events to form a coherent storyline. Utilizing these properties, along with thought-provoking questions and multi-level answers (answer and detailed causal explanation), our questions involve causal chains that interconnect multiple dynamic interactions between characters and visual scenes. These factors demand models to solve more challenging, yet well-defined causal relationships. We also introduce hard incorrect answer mining, including a causally confusing version that is even more challenging. While models perform well, there is much room for improvement, especially, on open-ended answers. We identify more advanced/explicit causal relationship modeling \& joint modeling of vision and language as the immediate areas for future efforts to focus upon. Along with the other complementary datasets, our new challenging dataset will pave the way for these developments in the field.