Existing neural response generation models have achieved impressive
improvements for two-party conversations, which assume that utterances are
sequentially organized. However, many real-world dialogues involve multiple
interlocutors and the structure of conversational context is much more complex,
e.g. utterances from different interlocutors can occur "in parallel". Facing
this challenge, there are works trying to model the relations among utterances
or interlocutors to facilitate response generation with clearer context.
Nonetheless, these methods rely heavily on such relations and all assume that
these are given beforehand, which is impractical and hinders the generality of
such methods. In this work, we propose to automatically infer the relations via
relational thinking on subtle clues inside the conversation context without any
human label, and leverage these relations to guide the neural response
generation. Specifically, we first apply a deep graph random process to fully
consider all possible relations among utterances in the conversational context.
Then the inferred relation graphs are integrated with a variational
auto-encoder framework to train a GAN for structure-aware response generation.
Experimental results on the Ubuntu Internet Relay Chat (IRC) channel benchmark
and the most recent Movie Dialogues show that our method outperforms various
baseline models for multi-party response generation.

该研究提出了一种通过对话上下文进行关系推理来指导神经响应生成的方法，并在多方对话中展示出优于基线模型的效果。

多方响应生成与关系解缠

Multi-party Response Generation with Relation Disentanglement

Visual Entailment with natural language explanations aims to infer the
relationship between a text-image pair and generate a sentence to explain the
decision-making process. Previous methods rely mainly on a pre-trained
vision-language model to perform the relation inference and a language model to
generate the corresponding explanation. However, the pre-trained
vision-language models mainly build token-level alignment between text and
image yet ignore the high-level semantic alignment between the phrases (chunks)
and visual contents, which is critical for vision-language reasoning. Moreover,
the explanation generator based only on the encoded joint representation does
not explicitly consider the critical decision-making points of relation
inference. Thus the generated explanations are less faithful to visual-language
reasoning. To mitigate these problems, we propose a unified Chunk-aware
Alignment and Lexical Constraint based method, dubbed as CALeC. It contains a
Chunk-aware Semantic Interactor (arr. CSI), a relation inferrer, and a Lexical
Constraint-aware Generator (arr. LeCG). Specifically, CSI exploits the sentence
structure inherent in language and various image regions to build chunk-aware
semantic alignment. Relation inferrer uses an attention-based reasoning network
to incorporate the token-level and chunk-level vision-language representations.
LeCG utilizes lexical constraints to expressly incorporate the words or chunks
focused by the relation inferrer into explanation generation, improving the
faithfulness and informativeness of the explanations. We conduct extensive
experiments on three datasets, and experimental results indicate that CALeC
significantly outperforms other competitor models on inference accuracy and
quality of generated explanations.

本文提出了一种名为 CALeC 的方法，旨在解决语言和图像理解中，以前的方法在语义和各种特征之间的对齐上存在问题，其包括一个基于块意识和词汇约束的生成器和一个基于关系的推理网络，用于提高视觉语言理解的精确度和生成的解释的质量。在三个数据集的广泛实验中，CALeC 明显优于其他竞争模型，可以准确推断出 text-image 对之间的关系并生成解释。

基于分块对齐和词汇限制的自然语言解释视觉蕴涵

Chunk-aware Alignment and Lexical Constraint for Visual Entailment with Natural Language Explanations

In this paper, we consider advancing web-scale knowledge extraction and
alignment by integrating OpenIE extractions in the form of (subject, predicate,
object) triples with Knowledge Bases (KB). Traditional techniques from
universal schema and from schema mapping fall in two extremes: either they
perform instance-level inference relying on embedding for (subject, object)
pairs, thus cannot handle pairs absent in any existing triples; or they perform
predicate-level mapping and completely ignore background evidence from
individual entities, thus cannot achieve satisfying quality. We propose OpenKI
to handle sparsity of OpenIE extractions by performing instance-level
inference: for each entity, we encode the rich information in its neighborhood
in both KB and OpenIE extractions, and leverage this information in relation
inference by exploring different methods of aggregation and attention. In order
to handle unseen entities, our model is designed without creating
entity-specific parameters. Extensive experiments show that this method not
only significantly improves state-of-the-art for conventional OpenIE
extractions like ReVerb, but also boosts the performance on OpenIE from
semi-structured data, where new entity pairs are abundant and data are fairly
sparse.

本文提出了 OpenKI 模型，通过对 OpenIE 三元组和 Knowledge Bases（KB）的整合来处理 OpenIE 的稀疏性。该模型通过进行实例级别推理来处理未知实体，并借助聚合和关注机制在关系推理中利用其邻域中的信息。实验结果表明，该方法不仅显着提高了现有 OpenIE 提取的准确率，而且还提高了面向半结构化数据的 OpenIE 表现。