| Challenge: | Existing methods to retrieve and browse memories are keyword based searches or catalog based browsing systems. |
| Approach: | They propose a conversational system which engages the user through a multi-modal, multi-turn dialog over the user’s memories. |
| Outcome: | The proposed system can perform QA over memories and make suggestions to surface related events or facts from past memories to make conversations more engaging and natural. |
Similar Papers
Navigating Connected Memories with a Task-oriented Dialog System (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent years have seen an increasing trend in the volume of personal media captured by users thanks to smartphones and smart glasses. |
| Approach: | They propose to use dialogs for connected memories to query media collection . they use a multimodal dialog simulator and manual paraphrasing to obtain natural language utterances. |
| Outcome: | The proposed dataset contains 11.5k userassistant dialogs grounded in simulated personal memory graphs. |
Dialog Generation Using Multi-Turn Reasoning Neural Networks (N18-1)
Copied to clipboard
| Challenge: | Existing methods for dialog generation are limited and short at generalization. |
| Approach: | They propose a generalizable dialog generation approach that adapts multi-turn reasoning to generate responses by taking current conversation session context as a document and current query as 'question' they separate the single memory used for document comprehension into different groups for speaker-specific topic and opinion embedding. |
| Outcome: | Experiments on Japanese 10-sentence (5-round) conversation modeling show that multi-turn reasoning can produce more diverse and acceptable responses than state-of-the-art single-turn and non-reasoning baselines. |
User Memory Reasoning for Conversational Recommendation (2020.coling-main)
Copied to clipboard
| Challenge: | Existing systems that update user preferences via asking relevant questions are unable to dynamically maintain and reason over their knowledge for current (and possibly future) recommendations. |
| Approach: | They propose a new memory graph (MG) -> Conversational Recommendation parallel corpus with 7K+ human-to-human role-playing dialogs and a graph-based reasoning model that updates MG from unstructured utterances and predicts optimal dialog policies based on updated MG. |
| Outcome: | The proposed model is based on a large-scale user memory bootstrapped from real-world user scenarios and can be easily updated from unstructured utterances. |
Multi-Grained Conversational Graph Network for Retrieval-based Dialogue Systems (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for retrieval-based dialogues concatenate all turns in the dialogue history as input, ignoring dialogue dependency and structural information between the utterances. |
| Approach: | They propose a multi-grained conversational graph network that considers multiple levels of abstraction from dialogue histories and semantic dependencies within multi-turn dialogues for addressing. |
| Outcome: | The proposed method improves on two benchmarks on open domain dialogues. |
Ditch the Gold Standard: Re-evaluating Conversational Question Answering (2022.acl-long)
Copied to clipboard
| Challenge: | Existing conversational question answering systems provide natural-language answers to users in information-seeking conversations. |
| Approach: | They conduct the first large-scale human evaluation of state-of-the-art conversational question answering systems . they propose a question rewriting mechanism based on predicted history which better correlates with human judgments . |
| Outcome: | The proposed question rewriting mechanism better correlates with human judgments. |
There Are a Thousand Hamlets in a Thousand People’s Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for knowledge selection focus on relevance between knowledge and dialogue context, ignoring personal preference for knowledge. |
| Approach: | They propose to introduce personal memory into knowledge selection in chatbots to address personalization issue by integrating personal memory and inverse mapping into a closed loop. |
| Outcome: | The proposed method outperforms existing methods significantly on automatic evaluation and human evaluation. |
D2PCM:A Multi-Turn Dialogue Dataset with Personalized Contextual Memory (2026.findings-acl)
Copied to clipboard
| Challenge: | Conventional interactive algorithms have predominantly treated memory as a contextual element, neglecting the nuanced cognitive processes involved in individualized memory encoding and retrieval. |
| Approach: | They propose a multi-turn dialogue dataset with Personalized Contextual Memory to facilitate advanced research on personalized memory processing. |
| Outcome: | The proposed datasets provide a comprehensive benchmark to facilitate advanced research on personalized memory processing. |
DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing knowledge grounding models focus on locating knowledge in document contexts that are relevant to the conversation. |
| Approach: | They propose a knowledge identification model that leverages document structure to provide dialogue-contextualized passage encodings and better locate knowledge relevant to the conversation. |
| Outcome: | The proposed model can be applied to document-grounded conversational datasets and shows generalization to unseen documents and long dialogue contexts. |
Multi-Level Memory for Task Oriented Dialogs (N19-1)
Copied to clipboard
| Challenge: | Recent task oriented dialog systems use memory architectures to incorporate external knowledge in their dialogs. |
| Approach: | They propose a novel multi-level memory architecture that separates dialog context and knowledge base results . they use cells for each query and their corresponding results to address queries . |
| Outcome: | The proposed architecture outperforms current state-of-the-art models on three publicly available data sets. |
Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Units (2024.lrec-main)
Copied to clipboard
| Challenge: | Successful conversations often rest on common understanding, says a researcher . despite recent advances in dialog systems, there is a noticeable deficit in their grounding capabilities . |
| Approach: | They propose to use a framework to build conversational grounding in dialogs . they propose to analyze two dialog corpora using grounding acts and grounding units . |
| Outcome: | The proposed model shows that language models are not enough to ground dialogs with machines . the proposed model can be used to test the performance of existing Language Models . |