Open Domain Multi-hop Question Answering faces a dual compositionality challenge: reasoning over complex query structures and integrating evidence scattered across contexts. Despite recent advancements in Graph-based Retrieval-Augmented Generation (GraphRAG), persistent limitations in complex reasoning and retrieval inaccuracies continue to constrain the efficacy of multi-hop QA systems. We introduce HiGraAgent, a framework that unifies graph-based retrieval with adaptive reasoning. It constructs a Hierarchical Knowledge Graph (HiGra) with entity alignment, reducing redundancy by 34.5% while preserving expressiveness; employs HiGraRetriever, a hybrid graph-semantic retriever that consistently outperforms the strongest graph-based method across benchmarks; and integrates a dual-agent adaptive reasoning protocol where a Seeker and a Librarian dynamically coordinate retrieval and reasoning. Together, these innovations enable HiGraAgent to achieve 85.3% average accuracy on HotpotQA, 2WikiMultihopQA, and MuSiQue, surpassing the strongest prior system by 11.7%. Our results highlight the importance of reframing multi-hop QA as a problem of adaptive reasoning, offering a more robust and flexible paradigm for complex information seeking.

In this work, we examine methods for data augmentation for text-based tasks such as neural machine translation (NMT). We formulate the design of a data augmentation policy with desirable properties as an optimization problem, and derive a generic analytic solution. This solution not only subsumes some existing augmentation schemes, but also leads to an extremely simple data augmentation strategy for NMT: randomly replacing words in both the source sentence and the target sentence with other random words from their corresponding vocabularies. We name this method SwitchOut. Experiments on three translation datasets of different scales show that SwitchOut yields consistent improvements of about 0.5 BLEU, achieving better or comparable performances to strong alternatives such as word dropout (Sennrich et al., 2016a). Code to implement this method is included in the appendix.

Recent advances in Neural Machine Translation (NMT) show that adding syntactic information to NMT systems can improve the quality of their translations. Most existing work utilizes some specific types of linguistically-inspired tree structures, like constituency and dependency parse trees. This is often done via a standard RNN decoder that operates on a linearized target tree structure. However, it is an open question of what specific linguistic formalism, if any, is the best structural representation for NMT. In this paper, we (1) propose an NMT model that can naturally generate the topology of an arbitrary tree structure on the target side, and (2) experiment with various target tree structures. Our experiments show the surprising result that our model delivers the best improvements with balanced binary trees constructed without any linguistic knowledge; this model outperforms standard seq2seq models by up to 2.1 BLEU points, and other methods for incorporating target-side syntax by up to 0.7 BLEU.