Update README.md
Browse files
README.md
CHANGED
@@ -5,10 +5,11 @@ license: apache-2.0
|
|
5 |
|
6 |
## Recent Update
|
7 |
- ππ» 2022.10.10: The repository `dstc11-simmc2.1-iflytek` for [DSTC11 Track1](https://github.com/facebookresearch/simmc2) is created.
|
8 |
-
|
9 |
|
10 |
## Overview
|
11 |
The [SIMMC2.1](https://github.com/facebookresearch/simmc2) challenge aims to lay the foundations for the real-world assistant agents that can handle multimodal inputs, and perform multimodal actions. It has 4 tasks: Ambiguous Candidate Identification, Multimodal Coreference Resolution, Multimodal Dialog State Tracking, Response Generation. We consider the joint input of textual context, tokenized objects and scene as multi-modal input, as well as compare the performance of single task training and multi task joint training.
|
|
|
12 |
|
13 |
## Model Date
|
14 |
Model was originally released in October 2022.
|
@@ -17,6 +18,20 @@ Model was originally released in October 2022.
|
|
17 |
The **mt-bart**, **mt-bart-sys** and **mt-bart-sys-nvattr** have the same model framework (transformer with multi-task head), which are finetuned on [SIMMC2.1](https://github.com/facebookresearch/simmc2) based on the pretrained [BART-Large](https://huggingface.co/facebook/bart-large) model. This [repository](https://github.com/scutcyr/dstc11-simmc2.1-iflytek) also contains code to finetune the model.
|
18 |
|
19 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
20 |
## Using with Transformers
|
21 |
(1) You should first download the model from huggingface used the scripts:
|
22 |
```bash
|
|
|
5 |
|
6 |
## Recent Update
|
7 |
- ππ» 2022.10.10: The repository `dstc11-simmc2.1-iflytek` for [DSTC11 Track1](https://github.com/facebookresearch/simmc2) is created.
|
8 |
+
- ππ» 2022.10.28: The model is public on huggingface, see the link [https://huggingface.co/scutcyr/dstc11-simmc2.1-iflytek](https://huggingface.co/scutcyr/dstc11-simmc2.1-iflytek) for detail.
|
9 |
|
10 |
## Overview
|
11 |
The [SIMMC2.1](https://github.com/facebookresearch/simmc2) challenge aims to lay the foundations for the real-world assistant agents that can handle multimodal inputs, and perform multimodal actions. It has 4 tasks: Ambiguous Candidate Identification, Multimodal Coreference Resolution, Multimodal Dialog State Tracking, Response Generation. We consider the joint input of textual context, tokenized objects and scene as multi-modal input, as well as compare the performance of single task training and multi task joint training.
|
12 |
+
As to subtask4, we also consider the system belief state (act and slot values) as the prombt for response generation. Non-visual metadata is also considered by adding the embedding to the object.
|
13 |
|
14 |
## Model Date
|
15 |
Model was originally released in October 2022.
|
|
|
18 |
The **mt-bart**, **mt-bart-sys** and **mt-bart-sys-nvattr** have the same model framework (transformer with multi-task head), which are finetuned on [SIMMC2.1](https://github.com/facebookresearch/simmc2) based on the pretrained [BART-Large](https://huggingface.co/facebook/bart-large) model. This [repository](https://github.com/scutcyr/dstc11-simmc2.1-iflytek) also contains code to finetune the model.
|
19 |
|
20 |
|
21 |
+
## Results
|
22 |
+
|
23 |
+
### devtest result
|
24 |
+
|
25 |
+
| Model | Subtask-1 Amb. Candi. F1 | Subtask-2 MM Coref F1 | Subtask-3 MM DST Slot F1 | Subtask-3 MM DST Intent F1 | Subtask-4 Response Gen. BLEU-4 |
|
26 |
+
|:----:|:----:|:----:|:----:|:----:|:----:|
|
27 |
+
| mt-bart-ensemble | 0.68466 | 0.77860 | 0.91816 | 0.97828 | 0.34496 |
|
28 |
+
| mt-bart-sys | | | | | 0.39064 |
|
29 |
+
| mt-bart-sys-attr | | | | | 0.38995 |
|
30 |
+
|
31 |
+
### teststd result
|
32 |
+
The teststd result is provided in the [teststd-result](https://github.com/scutcyr/dstc11-simmc2.1-iflytek/blob/main/results/teststd-result). One subfolder corresponds to one model.
|
33 |
+
|
34 |
+
|
35 |
## Using with Transformers
|
36 |
(1) You should first download the model from huggingface used the scripts:
|
37 |
```bash
|