{
  "id": "1709.03968",
  "version": "v1",
  "published": "2017-09-12T17:41:30.000Z",
  "updated": "2017-09-12T17:41:30.000Z",
  "title": "Affective Neural Response Generation",
  "authors": [
    "Nabiha Asghar",
    "Pascal Poupart",
    "Jesse Hoey",
    "Xin Jiang",
    "Lili Mou"
  ],
  "comment": "8 pages",
  "categories": [
    "cs.CL"
  ],
  "abstract": "Existing neural conversational models process natural language primarily on a lexico-syntactic level, thereby ignoring one of the most crucial components of human-to-human dialogue: its affective content. We take a step in this direction by proposing three novel ways to incorporate affective/emotional aspects into long short term memory (LSTM) encoder-decoder neural conversation models: (1) affective word embeddings, which are cognitively engineered, (2) affect-based objective functions that augment the standard cross-entropy loss, and (3) affectively diverse beam search for decoding. Experiments show that these techniques improve the open-domain conversational prowess of encoder-decoder networks by enabling them to produce emotionally rich responses that are more interesting and natural.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-09-12T17:41:30.000Z"
    }
  ],
  "analyses": {
    "subjects": [
      "68T50",
      "I.2.7"
    ],
    "keywords": [
      "affective neural response generation",
      "models process natural language",
      "process natural language primarily",
      "neural conversational models process natural"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 8,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}